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Wednesday, December 9, 2020

Sex differences in psychology

From Wikipedia, the free encyclopedia

Sex differences in psychology are differences in the mental functions and behaviors of the sexes, and are due to a complex interplay of biological, developmental, and cultural factors. Differences have been found in a variety of fields such as mental health, cognitive abilities, personality, emotion, sexuality, and tendency towards aggression. Such variation may be innate or learned and is often very difficult to distinguish. Modern research attempts to distinguish between these causes, and to analyze any ethical concerns raised. Since behavior is a result of interactions between nature and nurture researchers are interested in investigating how biology and environment interact to produce such differences, although this is often not possible.

A number of factors combine to influence the development of sex differences, including genetics and epigenetics; differences in brain structure and function; hormones, and socialization.

Definition

Psychological sex differences refer to emotional, motivational or cognitive differences between the sexes. Examples include a greater male tendencies toward violence, or that the female brain appears to have a strong affinity for empathy.

The terms "sex differences" and "gender differences" are at times used interchangeably, sometimes to refer to differences in male and female behaviors as either biological ("sex differences") or environmental/cultural ("gender differences"). This distinction is difficult to make owing to failures of parsing one from the other.

History

Beliefs about sex differences have likely existed throughout history. In his 1859 book On the Origin of Species Charles Darwin proposed that, like physical traits, psychological traits evolve through the process of sexual selection:

In the distant future I see open fields for far more important researches. Psychology will be based on a new foundation, that of the necessary acquirement of each mental power and capacity by gradation.

— Charles Darwin, The Origin of Species, 1859, p. 449.

Two of his later books, The Descent of Man, and Selection in Relation to Sex (1871) and The Expression of the Emotions in Man and Animals (1872) explore the subject of psychological differences between the sexes. The Descent of Man and Selection in Relation to Sex includes 70 pages on sexual selection in human evolution, some of which concerns psychological traits.

Psychological traits

Development of gender identity

Individuals who are sex reassigned at birth offer an opportunity to see what happens when a child who is genetically one sex is raised as the other. An infamous sexual reassignment case was that of David Reimer. Reimer was born biologically as a male but was raised as a female following medical advice after an operation that destroyed his genitalia. The reassignment was considered to be an especially valid test of the social learning concept of gender identity for several of the unique circumstances of the case. Despite the hormone therapies and surgeries, Reimer failed to identify as a female. According to his and his parents' accounts, the gender reassignment has caused severe mental problems throughout his life. At the age of 38, Reimer committed suicide.

Some individuals hold a different gender identity than that assigned at birth according to their sex, and are referred to as transgender. These cases often involve significant gender dysphoria. How these identities are formed is unknown, although some studies have suggested that male-to-female transgenderism is related to androgen levels during fetal development.

Childhood play

Many different studies have been conducted on sex differences in the play behavior of young children, often yielding conflicting results. One study conducted on nineteen-month-old children revealed a male preference for stereotypically "masculine" toys, and a female preference for stereotypically "feminine" toys, with males showing more variance in play behavior. A study of thirteen-month-old children supported the theory that males and females typically prefer toys typed to their gender, but instead found females showing more variance instead of males. An additional study found that a gendered divide in regards to toys may express itself as early as nine-months of age. Despite these apparent differences, a study of toddlers showed that both boys and girls were equally active when playing, and both sexes preferred toys that allowed them to express this.

The specific cause of this sex difference has also been investigated. A study with 112 boys and 100 girls found that the difference in play behavior appeared to be semi-correlated with fetal testosterone. Girls with congenital adrenal hyperplasia and thus exposed to high androgen levels during pregnancy tend to play more with male-typical toys and less with female-typical ones. However, some have argued that the characteristics of the condition itself could also result in those girls preferring different types of toys.

One study also claimed that one-day-old girls gaze longer at a face, whereas suspended mechanical mobiles, rather than a face, keep boys' attention for longer, though this study has been criticized as having methodological flaws. Research has shown that when male-typical toys are labeled as female-appropriate, young girls become significantly more likely to play with them. Certain studies have concluded that many end up treating infants and toddlers differently based on their assumed gender, even if boys and girls express the same behavior. Children raised by lesbian mothers were reported by the parents to be more androgynous in personality, suggesting that, if the reporting is accurate, upbringing could influence certain gendered traits.

Human-like play preferences have also been observed in guenon and rhesus macaques, though the co-author of the latter study warned about over-interpreting the data.

Sexual behavior

Psychological theories exist regarding the development and expression of gender differences in human sexuality. A number of these theories are consistent in predicting that men should be more approving of casual sex (sex happening outside a stable, committed relationship such as marriage) and should also be more promiscuous (have a higher number of sexual partners) than women:

A sociobiological approach applies evolutionary biology to human sexuality, emphasizing reproductive success in shaping patterns of sexual behavior. According to sociobiologists, since women's parental investment in reproduction is greater than men's, owing to human sperm being much more plentiful than eggs, and the fact that women must devote considerable energy to gestating their offspring, women will tend to be much more selective in their choice of mates than men. It may not be possible to accurately test sociobiological theories in relation to promiscuity and casual sex in contemporary (U.S.) society, which is quite different from the ancestral human societies in which most natural selection for sexual traits has occurred.

Neoanalytic theories are based on the observation that mothers, as opposed to fathers, bear the major responsibility for childcare in most families and cultures; both male and female infants therefore form an intense emotional attachment to their mother, a woman. According to feminist psychoanalytic theorist Nancy Chodorow, girls tend to preserve this attachment throughout life and define their identities in relational terms, whereas boys must reject this maternal attachment in order to develop a masculine identity. In addition, this theory predicts that women's economic dependence on men in a male-dominated society will tend to cause women to approve of sex more in committed relationships providing economic security, and less so in casual relationships.

The Sexual Strategies Theory by David Buss and David P. Schmitt is an evolutionary psychology theory regarding female and male short-term and long-term mating strategies which they argued are dependent on several different goals and vary depending on the environment.

According to social learning theory, sexuality is influenced by people's social environment. This theory suggests that sexual attitudes and behaviors are learned through observation of role models such as parents and media figures, as well as through positive or negative reinforcements for behaviors that match or defy established gender roles. It predicts that gender differences in sexuality can change over time as a function of changing social norms, and also that a societal double standard in punishing women more severely than men (who may in fact be rewarded) for engaging in promiscuous or casual sex will lead to significant gender differences in attitudes and behaviors regarding sexuality.

Such a societal double standard also figures in social role theory, which suggests that sexual attitudes and behaviors are shaped by the roles that men and women are expected to fill in society, and script theory, which focuses on the symbolic meaning of behaviors; this theory suggests that social conventions influence the meaning of specific acts, such as male sexuality being tied more to individual pleasure and macho stereotypes (therefore predicting a high number of casual sexual encounters) and female sexuality being tied more to the quality of a committed relationship.

Intelligence

With the advent of the concept of g, or general intelligence, some form of empirically measuring differences in intelligence, was possible, but results have been inconsistent. Studies have shown either no differences, or advantages for either sex. One study did find some advantage for women in later life, while another found that male advantages on some cognitive tests are minimized when controlling for socioeconomic factors. The differences in average IQ between women and men are small in magnitude and inconsistent in direction, although the variability of male scores has been found to be greater than that of females, resulting in more males than females in the top and bottom of the IQ distribution.

According to the 1995 report Intelligence: Knowns and Unknowns by the American Psychological Association, "Most standard tests of intelligence have been constructed so that there are no overall score differences between females and males." Arthur Jensen in 1998 conducted studies on sex differences in intelligence through tests that were "loaded heavily on g" but were not normed to eliminate sex differences. His conclusions he quoted were "No evidence was found for sex differences in the mean level of g. Males, on average, excel on some factors; females on others". Jensen's results that no overall sex differences existed for g has been strengthened by researchers who assessed this issue with a battery of 42 mental ability tests and found no overall sex difference.

Although most of the tests showed no difference, there were some that did. For example, they found females performed better on verbal abilities while males performed better on visuospatial abilities. One female advantage is in verbal fluency where they have been found to perform better in vocabulary, reading comprehension, speech production and essay writing. Males have been specifically found to perform better on spatial visualization, spatial perception, and mental rotation. Researchers had then recommended that general models such as fluid and crystallized intelligence be divided into verbal, perceptual and visuospatial domains of g, because when this model is applied then females excel at verbal and perceptual tasks while males on visuospatial tasks.

There are however also differences in the capacity of males and females in performing certain tasks, such as rotation of objects in space, often categorized as spatial ability. Other traditionally male advantages, such as in the field of mathematics are less clear. Although females have lesser performance in spatial abilities, they have better performance in processing speed involving letters, digits and rapid naming tasks, object location memory, verbal memory, and also verbal learning.

Memory

The results from research on sex differences in memory are mixed and inconsistent, as some studies show no difference, others show a female or male advantage. Most studies have found no sex differences in short-term memory, the rate of memory decline due to aging, or memory of visual stimuli. Females have been found to have an advantage in recalling auditory and olfactory stimuli, experiences, faces, names, and the location of objects in space. However, males show an advantage in recalling "masculine" events. A study examining sex differences in performance on the California Verbal Learning Test found that males performed better on Digit Span Backwards and on reaction time, while females were better on short-term memory recall and Symbol-Digit Modalities Test. Females have also demonstrated to have better verbal memory.

A study was conducted to explore regions within the brain that are activated during working memory tasks in males versus females. Four different tasks of increasing difficulty were given to 9 males and 8 females. Functional magnetic resonance imaging was used to measure brain activity. The lateral prefrontal cortices, the parietal cortices and caudates were activated in both genders. With more difficult tasks, more brain tissue was activated. The left hemisphere was predominantly activated in females' brains, whereas there was bilateral activation in males' brains.

Aggression

Although research on sex differences in aggression show that males are generally more likely to display aggression than females, how much of this is due to social factors and gender expectations is unclear. Aggression is closely linked with cultural definitions of "masculine" and "feminine". In some situations, women show equal or more aggression than men, although less physical; for example, women are more likely to use direct aggression in private, where other people cannot see them, and are more likely to use indirect aggression in public. Men are more likely to be the targets of displays of aggression and provocation than females. Studies by Bettencourt and Miller show that when provocation is controlled for, sex differences in aggression are greatly reduced. They argue that this shows that gender-role norms play a large part in the differences in aggressive behavior between men and women. Psychologist Anne Campbell argues that females are more likely to use indirect aggression, and that "cultural interpretations have 'enhanced' evolutionarily based sex differences by a process of imposition which stigmatises the expression of aggression by females and causes women to offer exculpatory (rather than justificatory) accounts of their own aggression".

According to the 2015 International encyclopedia of the social and behavioral sciences, sex differences in aggression is one of the most robust and oldest findings in psychology. Past meta-analyses in the encyclopedia found males regardless of age engaged in more physical and verbal aggression while small effect for females engaging in more indirect aggression such as rumor spreading or gossiping. It also found males tend to engage in more unprovoked aggression at higher frequency than females. This replicated another 2007 meta-analysis of 148 studies in the journal of Child Development which found greater male aggression in childhood and adolescence. This analysis also conforms with the Oxford Handbook of Evolutionary Psychology which reviewed past analysis and found greater male use in verbal and physical aggression with the difference being greater in the physical type. A meta-analysis of 122 studies published in the journal of Aggressive Behavior found males are more likely to cyber-bully than females. Difference also showed that females reported more cyber bullying behaviour during mid-adolescence while males showed more cyber bullying behaviour at late adolescence.

The relationship between testosterone and aggression is unclear, and a causal link has not been conclusively shown. Some studies indicate that testosterone levels may be affected by environmental and social influences. The relationship is difficult to study since the only reliable measure of brain testosterone is from a lumbar puncture which is not done for research purposes and many studies have instead used less reliable measures such as blood testosterone. In humans, males engage in crime and especially violent crime more than females. The involvement in crime usually rises in the early teens to mid teens which happen at the same time as testosterone levels rise. Most studies support a link between adult criminality and testosterone although the relationship is modest if examined separately for each sex. However, nearly all studies of juvenile delinquency and testosterone are not significant. Most studies have also found testosterone to be associated with behaviors or personality traits linked with criminality such as antisocial behavior and alcoholism. Nevertheless, researchers such as Lee Ellis have created the evolutionary neuroandrogenic theory to try to explain increased criminality in young men as being the result of sexual selection by females, pointing to testosterone as the mechanism by which this increased criminality occurs.

In species that have high levels of male physical competition and aggression over females, males tend to be larger and stronger than females. Humans have modest general body sexual dimorphism on characteristics such as height and body mass. However, this may understate the sexual dimorphism regarding characteristics related to aggression since females have large fat stores. The sex differences are greater for muscle mass and especially for upper body muscle mass. Men's skeleton, especially in the vulnerable face, is more robust. Another possible explanation, instead of intra-species aggression, for this sexual dimorphism may be that it is an adaption for a sexual division of labor with males doing the hunting. However, the hunting theory may have difficulty explaining differences regarding features such as stronger protective skeleton, beards (not helpful in hunting, but they increase the perceived size of the jaws and perceived dominance, which may be helpful in intra-species male competition), and greater male ability at interception (greater targeting ability can be explained by hunting).

There are evolutionary theories regarding male aggression in specific areas such as sociobiological theories of rape and theories regarding the high degree of abuse against stepchildren (the Cinderella effect). Another evolutionary theory explaining gender differences in aggression is the male warrior hypothesis, which explains that males have psychologically evolved for intergroup aggression in order to gain access to mates, resources, territory and status.

Personality traits

Cross-cultural research has shown population-level gender differences on the tests measuring sociability and emotionality. For example, on the scales measured by the Big Five personality traits women consistently report higher neuroticism, agreeableness, warmth (an extraversion facet) and openness to feelings, and men often report higher assertiveness (a facet of extraversion) and openness to ideas as assessed by the NEO-PI-R. Nevertheless, there is significant overlap in all these traits, so an individual woman may, for example, have lower neuroticism than the majority of men.

Gender differences in personality traits are largest in prosperous, healthy, and egalitarian cultures in which women have more opportunities that are equal to those of men. Differences in the magnitude of sex differences between more or less developed world regions were due to differences between men, not women, in these respective regions. That is, men in highly developed world regions were less neurotic, extroverted, conscientious and agreeable compared to men in less developed world regions. Women, on the other hand tended not to differ in personality traits across regions. Researchers have speculated that resource poor environments (that is, countries with low levels of development) may inhibit the development of gender differences, whereas resource rich environments facilitate them. This may be because males require more resources than females in order to reach their full developmental potential. The authors argued that due to different evolutionary pressures, men may have evolved to be more risk-taking and socially dominant, whereas women evolved to be more cautious and nurturant. Hunter-gatherer societies in which humans originally evolved may have been more egalitarian than later agriculturally oriented societies. Hence, the development of gender inequalities may have acted to constrain the development of gender differences in personality that originally evolved in hunter-gatherer societies. As modern societies have become more egalitarian again it may be that innate sex differences are no longer constrained and hence manifest more fully than in less developed cultures. Currently, this hypothesis remains untested, as gender differences in modern societies have not been compared with those in hunter-gatherer societies.

Normative Personality differences in the Cattell 16PF Domains. (Based on data in Del Giudice, M., Booth, T., & Irwing, P., 2012)

A personality trait directly linked to emotion and empathy where gender differences exist (see below) is scored on the Machiavellianism scale. Individuals who score high on this dimension are emotionally cool; this allows them to detach from others as well as values, and act egoistically rather than driven by affect, empathy or morality. In large samples of US college students males are on average more Machiavellian than females; in particular, males are over-represented among very high Machiavellians, while females are overrepresented among low Machiavellians. A 2014 meta-analysis by researchers Rebecca Friesdorf and Paul Conway found that men score significantly higher on narcissism than women and this finding is robust across past literature. The meta-analysis included 355 studies measuring narcissism across participants from the US, Germany, China, Netherlands, Italy, UK, Hong Kong, Singapore, Switzerland, Norway, Sweden, Australia and Belgium as well as measuring latent factors from 124 additional studies. The researchers noted that gender differences in narcissism is not just a measurement artifact but also represents true differences in the latent personality traits such as men's heightened sense of entitlement and authority.

Meta-analytic studies have also found males on average to be more assertive and having higher self-esteem. Females were on average higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). Women have also been found to be more punishment sensitive and men higher in sensation seeking and behavioural risk-taking. Deficits in effortful control also showed a very modest effect size in the male direction.

A meta-analysis of scientific studies concluded that men prefer working with things and women prefer working with people. When interests were classified by RIASEC type Holland Codes (Realistic, Investigative, Artistic, Social, Enterprising, Conventional), men showed stronger Realistic and Investigative interests, and women showed stronger Artistic, Social, and Conventional interests. Sex differences favoring men were also found for more specific measures of engineering, science, and mathematics interests.

Empathy

Current literature find that women demonstrate more empathy across studies. Women perform better than men in tests involving emotional interpretation, such as understanding facial expressions, and empathy.

Some studies argue that this is related to the subject's perceived gender identity and gender expectations. Additionally, culture impacts gender differences in the expression of emotions. This may be explained by the different social roles women and men have in different cultures, and by the status and power men and women hold in different societies, as well as the different cultural values various societies hold. Some studies have found no differences in empathy between women and men, and suggest that perceived gender differences are the result of motivational differences. Some researchers argue that because differences in empathy disappear on tests where it is not clear that empathy is being studied, men and women do not differ in ability, but instead in how empathetic they would like to appear to themselves and others.

A review published in the journal Neuropsychologia found that women are better at recognizing facial effects, expression processing and emotions in general. Men were only better at recognizing specific behaviour which includes anger, aggression and threatening cues. A 2006 meta-analysis by researcher Rena A Kirkland from the North American Journal of Psychology found significant sex differences favouring females in "Reading of the mind" test. "Reading of the mind" test is an ability measure of theory of mind or cognitive empathy in which Kirkland's analysis involved 259 studies across 10 countries. Another 2014 meta-analysis in the journal of Cognition and Emotion, found overall female advantage in non-verbal emotional recognition across 215 samples.

An analysis from the journal of Neuroscience & Biobehavioral Reviews found that there are sex differences in empathy from birth which remains consistent and stable across lifespan. Females were found to have higher empathy than males while children with higher empathy regardless of gender continue to be higher in empathy throughout development. Further analysis of brain tools such as event related potentials found that females who saw human suffering had higher ERP waveforms than males. Another investigation with similar brain tools such as N400 amplitudes found higher N400 in females in response to social situations which positively correlated with self-reported empathy. Structural fMRI studies found females have larger grey matter volumes in posterior inferior frontal and anterior inferior parietal cortex areas which are correlated with mirror neurons in fMRI literature. Females were also found to have stronger link between emotional and cognitive empathy. The researchers found that the stability of these sex differences in development are unlikely to be explained by any environment influences but rather might have some roots in human evolution and inheritance.

An evolutionary explanation for the difference is that understanding and tracking relationships and reading others' emotional states was particularly important for women in prehistoric societies for tasks such as caring for children and social networking. Throughout prehistory, females nurtured and were the primary caretakers of children so this might have led to an evolved neurological adaptation for women to be more aware and responsive to non-verbal expressions. According to the Primary Caretaker Hypothesis, prehistoric males did not have same selective pressure as primary caretakers so therefore this might explain modern day sex differences in emotion recognition and empathy.

Emotion

When measured with an affect intensity measure, women reported greater intensity of both positive and negative affect than men. Women also reported a more intense and more frequent experience of affect, joy, and love but also experienced more embarrassment, guilt, shame, sadness, anger, fear, and distress. Experiencing pride was more frequent and intense for men than for women. In imagined frightening situations, such as being home alone and witnessing a stranger walking towards your house, women reported greater fear. Women also reported more fear in situations that involved "a male's hostile and aggressive behavior" (281) In anger-eliciting situations, women communicated more intense feelings of anger than men. Women also reported more intense feelings of anger in relation to terrifying situations, especially situations involving a male protagonist. Emotional contagion refers to the phenomenon of a person's emotions becoming similar to those of surrounding people. Women have been reported to be more responsive to this.

Women are stereotypically more emotional and men are stereotypically angrier. When lacking substantial emotion information they can base judgments on, people tend to rely more on gender stereotypes. Results from a study conducted by Robinson and colleagues implied that gender stereotypes are more influential when judging others' emotions in a hypothetical situation.

There are documented differences in socialization that could contribute to sex differences in emotion and to differences in patterns of brain activity. An American Psychological Association article states that, "boys are generally expected to suppress emotions and to express anger through violence, rather than constructively". A child development researcher at Harvard University argues that boys are taught to shut down their feelings, such as empathy, sympathy and other key components of what is deemed to be pro-social behavior. According to this view, differences in emotionality between the sexes are theoretically only socially-constructed, rather than biological.

Context also determines a man or woman's emotional behavior. Context-based emotion norms, such as feeling rules or display rules, "prescribe emotional experience and expressions in specific situations like a wedding or a funeral", independent of the person's gender. In situations like a wedding or a funeral, the activated emotion norms apply to and constrain every person in the situation. Gender differences are more pronounced when situational demands are very small or non-existent as well as in ambiguous situations. During these situations, gender norms "are the default option that prescribes emotional behavior" (290-1).

Scientists in the field distinguish between emotionality and the expression of emotion: Associate Professor of psychology Ann Kring said, "It is incorrect to make a blanket statement that women are more emotional than men, it is correct to say that women show their emotions more than men." In two studies by Kring, women were found to be more facially expressive than men when it came to both positive and negative emotions. These researchers concluded that women and men experience the same amount of emotion, but that women are more likely to express their emotions.

Women are known to have anatomically differently shaped tear glands than men as well as having more of the hormone prolactin, which is present in tear glands, as adults. While girls and boys cry at roughly the same amount at age 12, by age 18, women generally cry four times more than men, which could be explained by higher levels of prolactin.

Women show significantly greater activity in the left amygdala when encoding and remembering emotionally disturbing pictures (such as mutilated bodies). Men and women tend to use different neural pathways to encode stimuli into memory. While highly emotional pictures were remembered best by all participants in one study, as compared to emotionally neutral images, women remembered the pictures better than men. This study also found greater activation of the right amygdala in men and the left amygdala in women. On average, women use more of the left cerebral hemisphere when shown emotionally arousing images, while men use more of their right hemisphere. Women also show more consistency between individuals for the areas of the brain activated by emotionally disturbing images.

A 2003 worldwide survey by the Pew Research Center found that overall women stated that they were somewhat happier than men with their lives. Compared to the previous report five years earlier women more often reported progress with their lives while men were more optimistic about the future. Women were more concerned about home and family issues than men who were more concerned about issues outside the home. Men were happier than women regarding family life and more optimistic regarding the children's future.

Research has shown that women are more likely than men to use emoticons in text messaging.

Ethics and morality

Meta-analysis on sex differences of moral orientation have found that women tend towards a more care based morality while men tend towards a more justice based morality. This is usually based on the fact that men have a more slight utilitarian reasoning while women have more deontological reasoning which is largely because of greater female affective response and rejection of harm-based behaviours. A meta-analysis published in the 2013 journal of Ethics and Behaviour after reviewing 19 primary studies also found women have greater moral sensitivity than men. A more recent large-scale (N = 336,691) analysis of sex differences using five moral principles of care, fairness, loyalty, authority, and purity (based on Moral Foundations Theory) suggested that women consistently score higher on care, fairness, and purity across 67 cultures. On the other hand, sex differences in loyalty and authority were small in size and highly variable across cultural contexts. This research, published in 2020 in Proceedings of the Royal Society B, also examined country-level sex differences in all moral foundations in relation to cultural, socioeconomic, and gender-related indicators revealing that global sex differences in moral foundations are larger in individualistic, Western, and gender-equal cultures. This is the first large-scale cross-cultural study showing that women score higher than men on fairness or justice-based moral intuition across many cultural contexts.

Mental health

Childhood conduct disorder and adult antisocial personality disorder as well as substance use disorders are more common in men. Many mood disorders, anxiety disorders, and eating disorders are more common in women. One explanation is that men tend to externalize stress while women tend to internalize it. Gender differences vary to some degree for different cultures. Women are more likely than men to show unipolar depression. One 1987 study found little empirical support for several proposed explanations, including biological ones, and argued that when depressed women tend to ruminate which may lower the mood further while men tend to distract themselves with activities. This may develop from women and men being raised differently.

Men and women do not differ on their overall rates of psychopathology; however, certain disorders are more prevalent in women, and vice versa. Women have higher rates of anxiety and depression (internalizing disorders) and men have higher rates of substance abuse and antisocial disorders (externalizing disorders). It is believed that divisions of power and the responsibilities set upon each sex are critical to this predisposition. Namely, women earn less money than men do, they tend to have jobs with less power and autonomy, and women are more responsive to problems of people in their social networks. These three differences can contribute to women's predisposition to anxiety and depression. It is suggested that socializing practices that encourage high self-regard and mastery would benefit the mental health of both women and men.

One study interviewed 18,572 respondents, aged 18 and over, about 15 phobic symptoms. These symptoms would yield diagnoses based on criteria for agoraphobia, social phobia, and simple phobia. Women had significantly higher prevalence rates of agoraphobia and simple phobia; however, there were no differences found between men and women in social phobia. The most common phobias for both women and men involved spiders, bugs, mice, snakes, and heights. The biggest differences between men and women in these disorders were found on the agoraphobic symptoms of "going out of the house alone" and "being alone", and on two simple phobic symptoms, involving the fear of "any harmless or dangerous animal" and "storms", with relatively more women having both phobias. There were no differences in the age of onset, reporting a fear on the phobic level, telling a doctor about symptoms, or the recall of past symptoms.

One study interviewed 2,181 people in Detroit, aged 18–45, seeking to explain gender differences in exposure to traumatic events and in the development or emergence of post traumatic stress disorder following this exposure. It was found that lifetime prevalence of traumatic events was a little higher in men than in women. However, following exposure to a traumatic event, the risk for PTSD was two times higher in women. It is believed this difference is due to the greater risk women have of developing PTSD after a traumatic event that involved assaultive violence. In fact, the probability of a woman developing PTSD following assaultive violence was 36% compared to 6% of men. The duration of PTSD is longer in women, as well.

Women and men are both equally likely at developing symptoms of schizophrenia, but the onset occurs earlier for men. It has been suggested that sexually dimorphic brain anatomy, the differential effects of estrogens and androgens, and the heavy exposure of male adolescents to alcohol and other toxic substances can lead to this earlier onset in men. It is believed that estrogens have a protective effect against the symptoms of schizophrenia. Although, it has been shown that other factors can contribute to the delayed onset and symptoms in women, estrogens have a large effect, as can be seen during a pregnancy. In pregnancy, estrogen levels are rising in women, so women who have had recurrent acute episodes of schizophrenia did not usually break down. However, after pregnancy, when estrogen levels have dropped, women tend to suffer from postpartum psychoses. Also, psychotic symptoms are exacerbated when, during the menstrual cycle, estrogen levels are at their lowest. In addition, estrogen treatment has yielded beneficial effects in patients with schizophrenia.

Pathological gambling has been known to have a higher prevalence rate, 2:1, in men to women. One study chose to identify gender-related differences by examining male and female gamblers, who were using a gambling helpline. There was 562 calls placed, and of this amount, 62.1% were men, and 37.9% were women. Male gamblers were more likely to report problems with strategic forms of gambling (blackjack or poker), and female gamblers were more likely to report problems with nonstrategic forms, such as slots or bingo. Male gamblers were also more likely to report a longer duration of gambling than women. Female gamblers were more likely to report receiving mental health treatment that was not related to gambling. Male gamblers were more likely to report a drug problem or being arrested on account of gambling. There were high rates of debt and psychiatric symptoms related to gambling observed in both groups of men and women.

There are also differences regarding gender and suicide. Males in Western societies are much more likely to die from suicide despite females having more suicide attempts.

The "extreme male brain theory" views autism and the Asperger syndrome as an extreme version of male-female differences regarding "systemizing" and empathizing abilities. The "imprinted brain theory" argues that autism and psychosis are contrasting disorders on a number of different variables and that this is caused by an unbalanced genomic imprinting favoring paternal genes (autism) or maternal genes (psychosis).

Cognitive control of behavior

Females tend to have a greater basal capacity to exert inhibitory control over undesired or habitual behaviors than males and respond differently to modulatory environmental contextual factors. For example, listening to music tends to significantly improve the rate of response inhibition in females, but reduce the rate of response inhibition in males. A 2010 meta-analyses found that women have small, but persistent, advantages in punishment sensitivity and effortful control across cultures. A 2014 review found that In humans, women discount more steeply than men, but sex differences on measures of impulsive action depend on tasks and subject samples.

Possible causes

Both biological and social/environmental factors have been studied for their impact on sex differences. Separating biological from environmental effects is difficult, and advocates for biological influences generally accept that social factors are also important.

Biology

Genetics

Psychological traits can vary between the sexes through sex-linkage. That is to say, what causes a trait may be related to the chromosomal sex of the individual. In contrast, there are also "sex-influenced" (or sex-conditioned) traits, in which the phenotypic manifestation of a gene depends on the sex of the individual. Even in a homozygous dominant or recessive female the condition may not be expressed fully. "Sex-limited" traits are characteristics only expressed in one sex. They may be caused by genes on either autosomal or sex chromosomes.

Evidence exists that there are sex-linked differences between the male and female brain.

Epigenetics

Epigenetic changes have also been found to cause sex-based differentiation in the brain. The extent and nature of these differences are not fully characterised. It has been shown that sex differences in some abilities (such as verbal processing, sensation seeking, speed in physical activities) are more apparent mostly in younger ages and subside after the age 30. Differences in socialization of males and females may decrease or increase the size of sex differences.

Brain structure and function

When it comes to the brain there are many similarities but also a number of differences in structure, neurotransmitters, and function. However, some argue that innate differences in the neurobiology of women and men have not been conclusively identified.

Structurally adult male brains are on average 11–12% heavier and 10% bigger than female brains. However, because men generally have a greater body mass than women, the brain-to-body mass ratio does not differ between the sexes. Other studies have stated bigger male brain size can only be partly accounted by body size. Researchers also found greater cortical thickness and cortical complexity in females and greater female cortical surface area after adjusting for brain volumes. Given that cortical complexity and cortical features are positively correlated with intelligence, researchers postulated that these differences might have evolved for females to compensate for smaller brain size and equalize overall cognitive abilities with males. Women have a greater developed neuropil or the space between neurons, which contains synapses, dendrites and axons and the cortex has neurons packed more closely together in the temporal and prefrontal cortex. Females also have greater cortical thickness in posterior temporal and inferior parietal regions compared to males independent of differences in brain or body size.

Though statistically there are sex differences in white matter and gray matter percentage, this ratio is directly related to brain size, and some argue these sex differences in gray and white matter percentage are caused by the average size difference between men and women. Others argue that these differences remain after controlling for brain volume.

In a 2013 meta-analysis, researchers found on average males had larger grey matter volume in bilateral amygdalae, hippocampi, anterior parahippocampal gyri, posterior cingulate gyri, precuneus, putamen and temporal poles, areas in the left posterior and anterior cingulate gyri, and areas in the cerebellum bilateral VIIb, VIIIa and Crus I lobes, left VI and right Crus II lobes. On the other hand, females on average had larger grey matter volume at the right frontal pole, inferior and middle frontal gyri, pars triangularis, planum temporale/parietal operculum, anterior cingulate gyrus, insular cortex, and Heschl's gyrus; bilateral thalami and precuneus; the left parahippocampal gyrus and lateral occipital cortex (superior division). The meta-analysis found larger volumes in females were most pronounced in areas in the right hemisphere related to language in addition to several limbic structures such as the right insular cortex and anterior cingulate gyrus.

Amber Ruigrok's 2013 meta-analysis also found greater grey matter density in the average male left amygdala, hippocampus, insula, pallidum, putamen, claustrum and right cerebellum. The meta-analysis also found greater grey matter density in the average female left frontal pole.

According to the neuroscience journal review series Progress in Brain Research, it has been found that males have larger and longer planum temporale and Sylvian fissure while females have significantly larger proportionate volumes to total brain volume in the superior temporal cortex, Broca's area, the hippocampus and the caudate. The midsagittal & fiber numbers in the anterior commissure that connect the temporal poles and mass intermedia that connects the thalami is also larger in women.

In the cerebral cortex, it has been observed that there is greater intra-lobe neural communication in male brains and greater inter-lobe (between the left and right hemispheres of the cerebral cortex) neural communication in female brains. In the cerebellum, the region of the brain that plays an important role in motor functions, males showed higher connectivity between hemispheres, and females showed higher connectivity within hemispheres. This potentially provides a neural basis for previous studies that showed sex-specific difference in certain psychological functions. Females on average outperform males on emotional recognition and nonverbal reasoning tests, while males outperform females on motor and spatial cognitive tests.

In the work of Szalkai et al. have computed structural (i.e., anatomical) connectomes of 96 subjects of the Human Connectome Project, and they have shown that in several deep graph-theoretical parameters, the structural connectome of women is significantly better connected than that of men. For example, women's connectome has more edges, higher minimum bipartition width, larger eigengap, greater minimum vertex cover than that of men. The minimum bipartition width (or the minimum balanced cut (see Cut (graph theory))) is a well-known measure of quality of computer multistage interconnection networks, it describes the possible bottlenecks in network communication: the higher this value is, the better is the network. The larger eigengap shows that the female connectome is a better expander graph than the connectome of males. The better expanding property, the higher minimum bipartition width and the greater minimum vertex cover show deep advantages in network connectivity in the case of female braingraph. Szalkai et al. have also shown that most of the deep graph theoretical differences remain in effect if big-brained women and small-brained men are compared: i.e., the graph theoretical differences are due to sex, and not the brain volume-differences of the subjects.

Hormones

Testosterone appears to be a major contributing factor to sexual motivation in male primates, including humans. The elimination of testosterone in adulthood has been shown to reduce sexual motivation in both male humans and male primates. Male humans who had their testicular function suppressed with a GnRH anatagonist displayed decreases in sexual desire and masturbation two weeks following the procedure. It is also suggested that levels of testosterone in men are related to the type of relationship in which they are involved. Men involved in polyamorous relationships display higher levels of testosterone than men involved in either a single partner relationship or single men.

Research on the ovulatory shift hypothesis explores differences in female mate preferences across the ovulatory cycle. Non-pill using heterosexual females who are ovulating (high levels of estrogens) were shown to have a preference for the scent of males with low levels of fluctuating asymmetry. Certain research has also indicated that ovulating heterosexual females display a preference toward masculine faces and report greater sexual attraction to males other than their current partner, though this has been called into question. A meta-analysis of 58 studies concluded that there was no evidence to support this theory. A different meta-analysis partially supported the hypothesis, but only in regards to "short-term" attractiveness. A later study of Finnish twins found that the influence of "context-dependent" factors (such as ovulation) on a female's attraction to masculine faces was less than one-percent.

Additionally, a 2016 paper suggested that any possible changes in preferences during ovulation would be moderated by the relationship quality itself, even to the point of inversion in favor of the female's current partner.

Culture

Fundamental sex differences in genetics, hormones and brain structure and function may manifest as distal cultural phenomena (e.g., males as primary combatants in warfare, the primarily female readership of romance novels, etc.). In addition, differences in socialization of males and females may have the effect of decreasing or increasing the magnitude of sex differences.

Artificial intelligence in healthcare

X-ray of a hand, with automatic calculation of bone age by a computer software

Artificial intelligence in healthcare is an overarching term used to describe the utilization of machine-learning algorithms and software, or artificial intelligence (AI), to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.

What distinguishes AI technology from traditional technologies in health care is the ability to gather data, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. These algorithms can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, (2) and some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used.

The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs are applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. AI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, and the British National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI software to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Currently, the United States government is investing billions of dollars to progress the development of Artificial Intelligence in healthcare. Companies are developing technologies that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.

As widespread use of AI in healthcare is relatively new, there are several unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases.

History

Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral. While it was designed for applications in organic chemistry, it provided the basis for a subsequent system MYCIN, considered one of the most significant early uses of artificial intelligence in medicine. MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners, however.

The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians. Approaches involving fuzzy set theory, Bayesian networks, and artificial neural networks, have been applied to intelligent computing systems in healthcare.

Medical and technological advancements occurring over this half-century period that have enabled the growth healthcare-related applications of AI include:

Current research

Various specialties in medicine have shown an increase in research regarding AI. As the novel coronavirus ravages through the globe, the United States is estimated to invest more than $2 billion in AI related healthcare research over the next 5 years, more than 4 times the amount spent in 2019 ($463 million). 

Radiology

AI is being studied within the radiology field to detect and diagnose diseases within patients through Computerized Tomography (CT) and Magnetic Resonance (MR) Imaging. The focus on Artificial Intelligence in radiology has rapidly increased in recent years according to the Radiology Society of North America, where they have seen growth from 0 to 3, 17, and overall 10% of total publications from 2015-2018 respectively. A study at Stanford created an algorithm that could detect pneumonia in patients with a better average F1 metric (a statistical metric based on accuracy and recall), than radiologists involved in the trial. Through imaging in oncology, AI has been able to serve well for detecting abnormalities and monitoring change over time; two key factors in oncological health. Many companies and vendor neutral systems such as icometrix, QUIBIM, Robovision, and UMC Utrecht’s IMAGRT have become available to provide a trainable machine learning platform to detect a wide range of diseases. The Radiological Society of North America has implemented presentations on AI in imaging during its annual conference. Many professionals are optimistic about the future of AI processing in radiology, as it will cut down on needed interaction time and allow doctors to see more patients. Although not always as good as a trained eye at deciphering malicious or benign growths, the history of medical imaging shows a trend toward rapid advancement in both capability and reliability of new systems. The emergence of AI technology in radiology is perceived as a threat by some specialists, as it can improve by certain statistical metrics in isolated cases, where specialists cannot.

Screening

Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.

In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists. On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.

In January 2020 researchers demonstrate an AI system, based on a Google DeepMind algorithm, that is capable of surpassing human experts in breast cancer detection.

In July 2020 it was reported that an AI algorithm by the University of Pittsburgh achieves the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity.

Psychiatry

In psychiatry, AI applications are still in a phase of proof-of-concept. Areas where the evidence is widening quickly include chatbots, conversational agents that imitate human behaviour and which have been studied for anxiety and depression.

Challenges include the fact that many applications in the field are developed and proposed by private corporations, such as the screening for suicidal ideation implemented by Facebook in 2017. Such applications outside the healthcare system raise various professional, ethical and regulatory questions.

Primary care

Primary care has become one key development area for AI technologies. AI in primary care has been used for supporting decision making, predictive modelling, and business analytics. Despite the rapid advances in AI technologies, general practitioners' view on the role of AI in primary care is very limited–mainly focused on administrative and routine documentation tasks.

Disease diagnosis

An article by Jiang, et al. (2017) demonstrated that there are several types of AI techniques that have been used for a variety of different diseases, such as support vector machines, neural networks, and decision trees. Each of these techniques is described as having a “training goal” so “classifications agree with the outcomes as much as possible…”.

To demonstrate some specifics for disease diagnosis/classification there are two different techniques used in the classification of these diseases include using “Artificial Neural Networks (ANN) and Bayesian Networks (BN)”. It was found that ANN was better and could more accurately classify diabetes and CVD.

Through the use of Medical Learning Classifiers (MLC’s), Artificial Intelligence has been able to substantially aid doctors in patient diagnosis through the manipulation of mass Electronic Health Records (EHR’s). Medical conditions have grown more complex, and with a vast history of electronic medical records building, the likelihood of case duplication is high. Although someone today with a rare illness is less likely to be the only person to have suffered from any given disease, the inability to access cases from similarly symptomatic origins is a major roadblock for physicians. The implementation of AI to not only help find similar cases and treatments, but also factor in chief symptoms and help the physicians ask the most appropriate questions helps the patient receive the most accurate diagnosis and treatment possible.

Telemedicine

An elderly man using a pulse oximeter to measure his blood oxygen levels

The increase of telemedicine, the treatment of patients remotely, has shown the rise of possible AI applications. AI can assist in caring for patients remotely by monitoring their information through sensors. A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of.

Another application of artificial intelligence is in chat-bot therapy. Some researchers charge that the reliance on chat-bots for mental healthcare does not offer the reciprocity and accountability of care that should exist in the relationship between the consumer of mental healthcare and the care provider (be it a chat-bot or psychologist), though.

Since the average age has risen due to a longer life expectancy, artificial intelligence could be useful in helping take care of older populations. Tools such as environment and personal sensors can identify a person’s regular activities and alert a caretaker if a behavior or a measured vital is abnormal. Although the technology is useful, there are also discussions about limitations of monitoring in order to respect a person’s privacy since there are technologies that are designed to map out home layouts and detect human interactions.

Electronic health records

Electronic health records (EHR) are crucial to the digitalization and information spread of the healthcare industry. Now that around 80% of medical practices use EHR, the next step is to use artificial intelligence to interpret the records and provide new information to physicians. One application uses natural language processing (NLP) to make more succinct reports that limit the variation between medical terms by matching similar medical terms. For example, the term heart attack and myocardial infarction mean the same things, but physicians may use one over the over based on personal preferences. NLP algorithms consolidate these differences so that larger datasets can be analyzed. Another use of NLP identifies phrases that are redundant due to repetition in a physician’s notes and keeps the relevant information to make it easier to read.

Beyond making content edits to an EHR, there are AI algorithms that evaluate an individual patient’s record and predict a risk for a disease based on their previous information and family history. One general algorithm is a rule-based system that makes decisions similarly to how humans use flow charts. This system takes in large amounts of data and creates a set of rules that connect specific observations to concluded diagnoses. Thus, the algorithm can take in a new patient’s data and try to predict the likeliness that they will have a certain condition or disease. Since the algorithms can evaluate a patient’s information based on collective data, they can find any outstanding issues to bring to a physician’s attention and save time. One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response. These methods are helpful due to the fact that the amount of online health records doubles every five years. Physicians do not have the bandwidth to process all this data manually, and AI can leverage this data to assist physicians in treating their patients.

Drug Interactions

Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken. To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms. Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were. Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms.

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization's VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.

Creation of new drugs

DSP-1181, a molecule of the drug for OCD (obsessive-compulsive disorder) treatment, was invented by artificial intelligence through joint efforts of Exscientia (British start-up) and Sumitomo Dainippon Pharma (Japanese pharmaceutical firm). The drug development took a single year, while pharmaceutical companies usually spend about five years on similar projects. DSP-1181 was accepted for a human trial.

In September 2019 Insilico Medicine reports the creation, via artificial intelligence, of six novel inhibitors of the DDR1 gene, a kinase target implicated in fibrosis and other diseases. The system, known as Generative Tensorial Reinforcement Learning (GENTRL), designed the new compounds in 21 days, with a lead candidate tested and showing positive results in mice.

The same month Canadian company Deep Genomics announces that its AI-based drug discovery platform has identified a target and drug candidate for Wilson's disease. The candidate, DG12P1, is designed to correct the exon-skipping effect of Met645Arg, a genetic mutation affecting the ATP7B copper-binding protein.

Industry

The trend of large health companies merging allows for greater health data accessibility. Greater health data lays the groundwork for implementation of AI algorithms.

A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems. As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions. Numerous companies are exploring the possibilities of the incorporation of big data in the healthcare industry. Many companies investigate the market opportunities through the realms of “data assessment, storage, management, and analysis technologies” which are all crucial parts of the healthcare industry.

The following are examples of large companies that have contributed to AI algorithms for use in healthcare:

  • IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. IBM is also working with CVS Health on AI applications in chronic disease treatment and with Johnson & Johnson on analysis of scientific papers to find new connections for drug development. In May 2017, IBM and Rensselaer Polytechnic Institute began a joint project entitled Health Empowerment by Analytics, Learning and Semantics (HEALS), to explore using AI technology to enhance healthcare.
  • Microsoft's Hanover project, in partnership with Oregon Health & Science University's Knight Cancer Institute, analyzes medical research to predict the most effective cancer drug treatment options for patients. Other projects include medical image analysis of tumor progression and the development of programmable cells.
  • Google's DeepMind platform is being used by the UK National Health Service to detect certain health risks through data collected via a mobile app. A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
  • Tencent is working on several medical systems and services. These include AI Medical Innovation System (AIMIS), an AI-powered diagnostic medical imaging service; WeChat Intelligent Healthcare; and Tencent Doctorwork
  • Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
  • Kheiron Medical developed deep learning software to detect breast cancers in mammograms.
  • Fractal Analytics has incubated Qure.ai which focuses on using deep learning and AI to improve radiology and speed up the analysis of diagnostic x-rays.
  • Elon Musk premiering the surgical robot that implants the Neuralink brain chip
    Neuralink has come up with a next generation neuroprosthetic which intricately interfaces with thousands of neural pathways in the brain. Their process allows a chip, roughly the size of a quarter, to be inserted in place of a chunk of skull by a precision surgical robot to avoid accidental injury.

Digital consultant apps like Babylon Health's GP at Hand, Ada Health, AliHealth Doctor You, KareXpert and Your.MD use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user's medical history. Entrepreneurs in healthcare have been effectively using seven business model archetypes to take AI solution to the marketplace. These archetypes depend on the value generated for the target user (e.g. patient focus vs. healthcare provider and payer focus) and value capturing mechanisms (e.g. providing information or connecting stakeholders).

IFlytek launched a service robot “Xiao Man”, which integrated artificial intelligence technology to identify the registered customer and provide personalized recommendations in medical areas. It also works in the field of medical imaging. Similar robots are also being made by companies such as UBTECH ("Cruzr") and Softbank Robotics ("Pepper").

The Indian startup Haptik recently developed a WhatsApp chatbot which answers questions associated with the deadly coronavirus in India.

With the market for AI expanding constantly, large tech companies such as Apple, Google, Amazon, and Baidu all have their own AI research divisions, as well as millions of dollars allocated for acquisition of smaller AI based companies. Many automobile manufacturers are beginning to use machine learning healthcare in their cars as well. Companies such as BMW, GE, Tesla, Toyota, and Volvo all have new research campaigns to find ways of learning a driver's vital statistics to ensure they are awake, paying attention to the road, and not under the influence of substances or in emotional distress.

Implications

The use of AI is predicted to decrease medical costs as there will be more accuracy in diagnosis and better predictions in the treatment plan as well as more prevention of disease.

Other future uses for AI include Brain-computer Interfaces (BCI) which are predicted to help those with trouble moving, speaking or with a spinal cord injury. The BCIs will use AI to help these patients move and communicate by decoding neural activates.

Artificial intelligence has led to significant improvements in areas of healthcare such as medical imaging, automated clinical decision-making, diagnosis, prognosis, and more. Although AI possesses the capability to revolutionize several fields of medicine, it still has limitations and cannot replace a bedside physician.

Healthcare is a complicated science that is bound by legal, ethical, regulatory, economical, and social constraints. In order to fully implement AI within healthcare, there must be "parallel changes in the global environment, with numerous stakeholders, including citizen and society."

Expanding care to developing nations

Artificial intelligence continues to expand in its abilities to diagnose more people accurately in nations where fewer doctors are accessible to the public.  Many new technology companies such as Spacex and the Raspberry Pi Foundation have enabled more developing countries to have access to computers and the internet than ever before. With the increasing capabilities of AI over the internet, advanced machine learning algorithms can allow patients to get accurately diagnosed when they would previously have no way of knowing if they had a life threatening disease or not.

Using AI in developing nations who do not have the resources will diminish the need for outsourcing and can improve patient care. AI can allow for not only diagnosis of patient is areas where healthcare is scarce, but also allow for a good patient experience by resourcing files to find the best treatment for a patient. The ability of AI to adjust course as it goes also allows the patient to have their treatment modified based on what works for them; a level of individualized care that is nearly non-existent in developing countries.

Regulation

While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations.

A man speaking at the GDPR compliance workshop at the 2019 Entrepreneurship Summit.

Currently, there are regulations pertaining to the collection of patient data. This includes policies such as the Health Insurance Portability and Accountability Act (HIPPA) and the European General Data Protection Regulation (GDPR). The GDPR pertains to patients within the EU and details the consent requirements for patient data use when entities collect patient healthcare data. Similarly, HIPPA protects healthcare data from patient records in the United States. In May 2016, the White House announced its plan to host a series of workshops and formation of the National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence. In October 2016, the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development (within government and academia). The report notes a strategic R&D plan for the subfield of health information technology is in development stages.

The only agency that has expressed concern is the FDA. Bakul Patel, the Associate Center Director for Digital Health of the FDA, is quoted saying in May 2017:

“We're trying to get people who have hands-on development experience with a product's full life cycle. We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”

The joint ITU-WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) has built a platform for the testing and benchmarking of AI applications in health domain. As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.

Ethical concerns

Data collection

In order to effectively train Machine Learning and use AI in healthcare, massive amounts of data must be gathered. Acquiring this data, however, comes at the cost of patient privacy in most cases and is not well received publicly. For example, a survey conducted in the UK estimated that 63% of the population is uncomfortable with sharing their personal data in order to improve artificial intelligence technology. The scarcity of real, accessible patient data is a hindrance that deters the progress of developing and deploying more artificial intelligence in healthcare.

Automation

According to a recent study, AI can replace up to 35% of jobs in the UK within the next 10 to 20 years. However, of these jobs, it was concluded that AI has not eliminated any healthcare jobs so far. Though if AI were to automate healthcare related jobs, the jobs most susceptible to automation would be those dealing with digital information, radiology, and pathology, as opposed to those dealing with doctor to patient interaction.

Automation can provide benefits alongside doctors as well. It is expected that doctors who take advantage of AI in healthcare will provide greater quality healthcare than doctors and medical establishments who do not. AI will likely not completely replace healthcare workers but rather give them more time to attend to their patients. AI may avert healthcare worker burnout and cognitive overload

AI will ultimately help contribute to progression of societal goals which include better communication, improved quality of healthcare, and autonomy.

Bias

Since AI makes decisions solely on the data it receives as input, it is important that this data represents accurate patient demographics. In a hospital setting, patients do not have full knowledge of how predictive algorithms are created or calibrated. Therefore, these medical establishments can unfairly code their algorithms to discriminate against minorities and prioritize profits rather than providing optimal care.

There can also be unintended bias in these algorithms that can exacerbate social and healthcare inequities.  Since AI’s decisions are a direct reflection of its input data, the data it receives must have accurate representation of patient demographics. White males are overly represented in medical data sets. Therefore, having minimal patient data on minorities can lead to AI making more accurate predictions for majority populations, leading to unintended worse medical outcomes for minority populations. Collecting data from minority communities can also lead to medical discrimination. For instance, HIV is a prevalent virus among minority communities and HIV status can be used to discriminate against patients.  However, these biases are able to be eliminated through careful implementation and a methodical collection of representative data.

Representation of a Lie group

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