A digital ally: The potential roles of ChatGPT in mental health services

Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review

BACKGROUND

Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns.

OBJECTIVE

We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues.

METHODS

We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher's Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme.

RESULTS

We included 101 articles, of which 95% (n=96) were published in 2018 or later. Most were reviews (n=22, 21.8%) followed by commentaries (n=17, 16.8%). The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of "black box" algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers' jobs (n=16, 15.8%). Other themes were discussed in 9.9% (n=10) of the identified articles.

CONCLUSIONS

Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders' perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability. Addressing these gaps can inform normative analysis and guide the development of ethical guidelines for responsible CAI use in mental health care.

Applications of large language models in psychiatry: a systematic review

Background

With their unmatched ability to interpret and engage with human language and context, large language models (LLMs) hint at the potential to bridge AI and human cognitive processes. This review explores the current application of LLMs, such as ChatGPT, in the field of psychiatry.

Methods

We followed PRISMA guidelines and searched through PubMed, Embase, Web of Science, and Scopus, up until March 2024.

Results

From 771 retrieved articles, we included 16 that directly examine LLMs' use in psychiatry. LLMs, particularly ChatGPT and GPT-4, showed diverse applications in clinical reasoning, social media, and education within psychiatry. They can assist in diagnosing mental health issues, managing depression, evaluating suicide risk, and supporting education in the field. However, our review also points out their limitations, such as difficulties with complex cases and potential underestimation of suicide risks.

Conclusion

Early research in psychiatry reveals LLMs' versatile applications, from diagnostic support to educational roles. Given the rapid pace of advancement, future investigations are poised to explore the extent to which these models might redefine traditional roles in mental health care.

Potential use of large language models for mitigating students' problematic social media use: ChatGPT as an example

The problematic use of social media has numerous negative impacts on individuals' daily lives, interpersonal relationships, physical and mental health, and more. Currently, there are few methods and tools to alleviate problematic social media, and their potential is yet to be fully realized. Emerging large language models (LLMs) are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life. In mitigating problematic social media use, LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users, providing personalized information and resources, monitoring and intervening in problematic social media use, and more. In this process, we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT, leveraging their advantages to better address problematic social media use, while also acknowledging the limitations and potential pitfalls of ChatGPT technology, such as errors, limitations in issue resolution, privacy and security concerns, and potential overreliance. When we leverage the advantages of LLMs to address issues in social media usage, we must adopt a cautious and ethical approach, being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

PURPOSE OF REVIEW

ChatGPT has emerged as a potentially useful tool for healthcare. Its role in urology is in its infancy and has much potential for research, clinical practice and for patient assistance. With this narrative review, we want to draw a picture of what is known about ChatGPT's integration in urology, alongside future promises and challenges.

RECENT FINDINGS

The use of ChatGPT can ease the administrative work, helping urologists with note-taking and clinical documentation such as discharge summaries and clinical notes. It can improve patient engagement through increasing awareness and facilitating communication, as it has especially been investigated for uro-oncological diseases. Its ability to understand human emotions makes ChatGPT an empathic and thoughtful interactive tool or source for urological patients and their relatives. Currently, its role in clinical diagnosis and treatment decisions is uncertain, as concerns have been raised about misinterpretation, hallucination and out-of-date information. Moreover, a mandatory regulatory process for ChatGPT in urology is yet to be established.

SUMMARY

ChatGPT has the potential to contribute to precision medicine and tailored practice by its quick, structured responses. However, this will depend on how well information can be obtained by seeking appropriate responses and asking the pertinent questions. The key lies in being able to validate the responses, regulating the information shared and avoiding misuse of the same to protect the data and patient privacy. Its successful integration into mainstream urology needs educational bodies to provide guidelines or best practice recommendations for the same.

ChatGPT: Opportunities, risks and priorities for psychiatry

The advancement of large language models such as ChatGPT, opens new possibilities in psychiatry but also invites scrutiny. This paper examines the potential opportunities, risks, and crucial areas of focus within this area. The active engagement of the mental health community is seen as critical to ensure ethical practice, equal access, and a patient-centric approach.

Appraising the performance of ChatGPT in psychiatry using 100 clinical case vignettes

BACKGROUND

ChatGPT has emerged as the most advanced and rapidly developing large language chatbot system. With its immense potential ranging from answering a simple query to cracking highly competitive medical exams, ChatGPT continues to impress the scientists and researchers worldwide giving room for more discussions regarding its utility in various fields. One such field of attention is Psychiatry. With suboptimal diagnosis and treatment, assuring mental health and well-being is a challenge in many countries, particularly developing nations. To this regard, we conducted an evaluation to assess the performance of ChatGPT 3.5 in Psychiatry using clinical cases to provide evidence-based information regarding the implication of ChatGPT 3.5 in enhancing mental health and well-being.

METHODS

ChatGPT 3.5 was used in this experimental study to initiate the conversations and collect responses to clinical vignettes in Psychiatry. Using 100 clinical case vignettes, the replies were assessed by expert faculties from the Department of Psychiatry. There were 100 different psychiatric illnesses represented in the cases. We recorded and assessed the initial ChatGPT 3.5 responses. The evaluation was conducted using the objective of questions that were put forth at the conclusion of the case, and the aim of the questions was divided into 10 categories. The grading was completed by taking the mean value of the scores provided by the evaluators. Graphs and tables were used to represent the grades.

RESULTS

The evaluation report suggests that ChatGPT 3.5 fared extremely well in Psychiatry by receiving "Grade A" ratings in 61 out of 100 cases, "Grade B" ratings in 31, and "Grade C" ratings in 8. Majority of the queries were concerned with the management strategies, which were followed by diagnosis, differential diagnosis, assessment, investigation, counselling, clinical reasoning, ethical reasoning, prognosis, and request acceptance. ChatGPT 3.5 performed extremely well, especially in generating management strategies followed by diagnoses for different psychiatric conditions. There were no responses which were graded "D" indicating that there were no errors in the diagnosis or response for clinical care. Only a few discrepancies and additional details were missed in a few responses that received a "Grade C" CONCLUSION: It is evident from our study that ChatGPT 3.5 has appreciable knowledge and interpretation skills in Psychiatry. Thus, ChatGPT 3.5 undoubtedly has the potential to transform the field of Medicine and we emphasize its utility in Psychiatry through the finding of our study. However, for any AI model to be successful, assuring the reliability, validation of information, proper guidelines and implementation framework are necessary.