Artificial intelligence is increasingly being used in medical practice to complete tasks that were previously completed by the physician, such as visit documentation, treatment plans and discharge summaries. As artificial intelligence becomes a routine part of medical care, physicians increasingly trust and rely on its clinical recommendations. However, there is concern that some physicians, especially those younger and less experienced, will become over-reliant on artificial intelligence. Over-reliance on it may reduce the quality of clinical reasoning and decision-making, negatively impact patient communications and raise the potential for deskilling. As artificial intelligence becomes a routine part of medical treatment, it is imperative that physicians recognise the limitations of artificial intelligence tools. These tools may assist with basic administrative tasks but cannot replace the uniquely human interpersonal and reasoning skills of physicians. The purpose of this feature article is to discuss the risks of physician deskilling based on increasing reliance on artificial intelligence.

The use of Generative Artificial Intelligence (GenAI) by teenagers is increasing rapidly. GenAI is a form of artificial intelligence that creates new text, images, video and audio, using models based on huge amounts of training data. However, using GenAI can also create misinformation and biased, inappropriate and harmful outputs. Teenagers are increasingly using GenAI in daily life, including in mental healthcare, and may not be aware of the limitations and risks. GenAI may also be used for malicious purposes that may have long-term, negative impacts on mental health. There is a need to increase awareness of how GenAI may have a negative impact on the mental health of teenagers.

Recently a new type of generative artificial intelligence, agentic artificial intelligence, has been developed that extends product capabilities to act autonomously. Although agentic artificial intelligence systems offer a unique approach to supporting physicians, the products also pose many validity, privacy and security risks. Physicians need to understand the basic features of agentic artificial intelligence.

Online platforms and activities, including smartphones, computers, social media, video games and applications involving artificial intelligence, have become a regular part of daily life and offer individuals a wide range of benefits. The purpose of this document is to increase psychiatrists' awareness of the frequency and potential risks associated with excessive internet use, and to emphasise the need for psychiatrists to routinely question patients about their online activities. Internet use may become excessive and result in both psychological distress and physical impairments. Treatments and countermeasures may be required to address the harmful consequences of excessive internet use. Psychiatrists should be aware of patient online activities. Understanding of a patient's online behaviour should now be a routine part of a psychiatric interview.

Both patients and physicians are routinely exposed to the corporate promotion of artificial intelligence (AI) for healthcare products. Hype for AI products may impact both patient behavior and attitudes about healthcare. Corporate AI hype may intentionally overlook the known limitations associated with AI products and focus solely on potential benefits. As AI is increasingly integrated into medicine, physicians are also routinely subject to AI hype. As the promotion and use of AI products have grown dramatically in recent years, physicians should be aware of the potential benefits and risks of AI products despite the hype.

Chatbots are widely used by business in everyday life to interact with consumers in e-commerce, social networking, education, and government settings. Anthropomorphism is a fundamental aspect of chatbot design and implementation, and increases the interaction of chatbots with humans. As part of the recent expansion of artificial intelligence (AI) technologies, chatbots incorporated AI to engage more personally with consumers. The anthropomorphic characteristics of chatbots, including both visual appearance and language, influence the interaction of chatbots with humans in diverse ways that may have both positive and negative consequences. The purpose of this review is to increase physician awareness of the use of chatbots which use AI to enhance anthropomorphism. Consumers routinely anthropomorphize products including chatbots. Physicians need to be aware of the wide-ranging and routine use of chatbots, and the importance of anthropomorphism in chatbot design and implementation. Physicians also need to recognize potential negative consequences of anthropomorphism of chatbots on mental health.

The emphasis on artificial intelligence (AI) is rapidly increasing across many diverse aspects of society. This manuscript discusses some of the key topics related to the expansion of AI. These include a comparison of the unique cognitive capabilities of human intelligence with AI, and the potential risks of using AI in clinical medicine. The general public attitudes towards AI are also discussed, including patient perspectives. As the promotion of AI in high-risk situations such as clinical medicine expands, the limitations, risks and benefits of AI need to be better understood.

BackgroundThe rate of suicide attempts by patients with bipolar disorder is high. In addition to patient and country specific factors, environmental factors may contribute to suicidal behavior. Sunlight has multiple diverse impacts on human physiology and behavior. Solar insolation is defined as the electromagnetic energy from the sun striking a surface area on earth. We previously found that a large change in solar insolation between the minimum and maximum monthly values was associated with an increased risk of suicide attempts in patients with bipolar I disorder.MethodsThe association between solar insolation and a history of suicide attempts in bipolar disorder was again investigated using an international database with 15% more data and more sites at diverse locations and countries.ResultsData were available from 5641 patients with bipolar I disorder living at a wide range of latitudes in 41 countries in both hemispheres. A large change in solar insolation between the minimum and maximum monthly values was associated with a history of suicide attempts in patients with bipolar I disorder, a replication of our prior analysis. The estimated model also associated state sponsored religion in the onset country, female gender, a history of alcohol or substance abuse, and being part of a younger birth cohort with a history of suicide attempts.ConclusionsA large change between the minimum and maximum monthly values of solar insolation was associated with a history of suicide attempts in bipolar I disorder, replicating our prior research. Physicians should be aware that daylight has wide ranging physiological and psychiatric impacts, and that living with large changes in solar insolation may be associated with an increased suicide risk.

Summary With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.

This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.

Abstract Background Sunlight contains ultraviolet B (UVB) radiation that triggers the production of vitamin D by skin. Vitamin D has widespread effects on brain function in both developing and adult brains. However, many people live at latitudes (about > 40 N or S) that do not receive enough UVB in winter to produce vitamin D. This exploratory study investigated the association between the age of onset of bipolar I disorder and the threshold for UVB sufficient for vitamin D production in a large global sample. Methods Data for 6972 patients with bipolar I disorder were obtained at 75 collection sites in 41 countries in both hemispheres. The best model to assess the relation between the threshold for UVB sufficient for vitamin D production and age of onset included 1 or more months below the threshold, family history of mood disorders, and birth cohort. All coefficients estimated at P ≤ 0.001. Results The 6972 patients had an onset in 582 locations in 70 countries, with a mean age of onset of 25.6 years. Of the onset locations, 34.0% had at least 1 month below the threshold for UVB sufficient for vitamin D production. The age of onset at locations with 1 or more months of less than or equal to the threshold for UVB was 1.66 years younger. Conclusion UVB and vitamin D may have an important influence on the development of bipolar disorder. Study limitations included a lack of data on patient vitamin D levels, lifestyles, or supplement use. More study of the impacts of UVB and vitamin D in bipolar disorder is needed to evaluate this supposition.

Telepsychiatry practiced by psychiatrists is evidence-based, regulated, private, and effective in diverse settings. The use of telemedicine has grown since the COVID-19 pandemic as people routinely obtain more healthcare services online. At the same time, there has been a rapid increase in the number of digital mental health startups that offer various services including online therapy and access to prescription medications. These digital mental health firms advertise directly to the consumer primarily through digital advertising. The purpose of this narrative review is to contrast traditional telepsychiatry and the digital mental health market related to online therapy.In contrast to standard telepsychiatry, most of the digital mental health startups are unregulated, have unproven efficacy, and raise concerns related to self-diagnosis, self-medicating, and inappropriate prescribing. The role of digital mental health firms for people with serious mental illness has not been determined. There are inadequate privacy controls for the digital mental health firms, including for online therapy. We live in an age where there is widespread admiration for technology entrepreneurs and increasing emphasis on the role of the patient as a consumer. Yet, the business practices of digital mental health startups may compromise patient safety for profits. There is a need to address issues with the digital mental health startups and to educate patients about the differences between standard medical care and digital mental health products.

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Purpose of Review Emotion artificial intelligence (AI) is technology for emotion detection and recognition. Emotion AI is expanding rapidly in commercial and government settings outside of medicine, and will increasingly become a routine part of daily life. The goal of this narrative review is to increase awareness both of the widespread use of emotion AI, and of the concerns with commercial use of emotion AI in relation to people with mental illness. Recent Findings This paper discusses emotion AI fundamentals, a general overview of commercial emotion AI outside of medicine, and examples of the use of emotion AI in employee hiring and workplace monitoring. The successful re-integration of patients with mental illness into society must recognize the increasing commercial use of emotion AI. There are concerns that commercial use of emotion AI will increase stigma and discrimination, and have negative consequences in daily life for people with mental illness. Commercial emotion AI algorithm predictions about mental illness should not be treated as medical fact.

Objective: Circadian rhythm disruption is commonly observed in bipolar disorder (BD). Daylight is the most powerful signal to entrain the human circadian clock system. This exploratory study investigated if solar inso-lation at the onset location was associated with the polarity of the first episode of BD I. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area of the Earth. Methods: Data from 7488 patients with BD I were collected at 75 sites in 42 countries. The first episode occurred at 591 onset locations in 67 countries at a wide range of latitudes in both hemispheres. Solar insolation values were obtained for every onset location, and the ratio of the minimum mean monthly insolation to the maximum mean monthly insolation was calculated. This ratio is largest near the equator (with little change in solar insolation over the year), and smallest near the poles (where winter insolation is very small compared to summer insolation). This ratio also applies to tropical locations which may have a cloudy wet and clear dry season, rather than winter and summer. Results: The larger the change in solar insolation throughout the year (smaller the ratio between the minimum monthly and maximum monthly values), the greater the likelihood the first episode polarity was depression. Other associated variables were being female and increasing percentage of gross domestic product spent on country health expenditures. (All coefficients: P <= 0.001). Conclusion: Increased awareness and research into circadian dysfunction throughout the course of BD is warranted.