Generative artificial intelligence in mental health care: potential benefits and current challenges

Torous, John; Blease, Charlotte

doi:10.1002/wps.21148

Cited by 8 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, data collection in digital phenotyping involves tracking patients beyond traditional health information and can involve data such as social media posts, geolocation, and telephone and SMS text message traffic (among other data) that can provide revealing insights about the daily lives of individuals [107,108]. Studies also show that there are significant limitations with clinicians' awareness of the ethical considerations regarding artificial intelligence-powered innovations in health care [109][110][111][112][113] (Textbox 5). Furthermore, health laws have not kept up with digital technologies, although authorities have recently made efforts to regulate technology while also aiming to protect civil liberties and rights to privacy [107,108].…”

Section: Regulationmentioning

confidence: 99%

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

Smith,

Hardy,

Vinnikova

et al. 2024

JMIR Ment Health

Self Cite

View full text Add to dashboard Cite

Background Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI). Objective An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI. Methods The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group. Results Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI. Conclusions The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

show abstract

Section: Regulationmentioning

confidence: 99%

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

Smith,

Hardy,

Vinnikova

et al. 2024

JMIR Ment Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…To be successful, AI tools must be integrated into existing health systems (e.g. electronic health records (EHR)) across varying healthcare settings (Alowais et al, 2023;Torous & Blease, 2024) that have the ability to easily communicate with one another. As EHR companies seek to expand and leverage AI technologies, questions that span ED prevention, diagnosis, and treatment, and likely many other areas of health, will need to be explored fully to understand the impact of advancement and associated risks (Epic).…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Aside from concerns relating to operationalization and implementation, AI programs will need to be able to ideally assimilate information from multiple sources, ranging from patient and caregiver collected outcome measures, collection of biologic variables including "omic" data and neuroimaging, as well as information gleaned from clinical encounters as a means of ultimately building and testing diagnostic and treatment approaches that better reflect the underlying nature of mental illness including EDs (Torous & Blease, 2024).…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Of the studies included, many were retrospective and had a high risk for bias without evidence of external validation, while less than 1/3 of the studies used original data (Tornero-Costa et al, 2023). Concerns relating to patient privacy are also paramount given the increase in the development and use of chatbots and their current inability to protect sensitive personal health information (PHI) (American Psychiatric Association, 2023; Torous & Blease, 2024).…”

Section: Governance Of Ai: Issues Relating To Bias Ethics and Safetymentioning

confidence: 99%

See 1 more Smart Citation

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders

Norris,

Obeid,

El‐Emam

2024

Intl J Eating Disorders

View full text Add to dashboard Cite

ObjectiveTo provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research.MethodAn overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided.ResultsIn addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets.DiscussionThere is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice.Public SignificanceArtificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.

show abstract

“…Introducing actual cases of ChatGPT’s use in diagnosing specific diseases, patient consultation, or medical data analysis not only provides readers with a more concrete and vivid understanding but also helps assess the technology’s effectiveness and feasibility in real medical environments 4 . It has been reported that ChatGPT is widely discussed and used in medical fields such as urology and psychiatry 5 , 6 , where it contributes to enhancing the work experience of healthcare professionals. Moreover, case analyses can demonstrate how ChatGPT solves specific medical problems, such as improving diagnostic accuracy, creating personalized treatment plans, and optimizing the allocation of medical resources.…”

mentioning

confidence: 99%

ChatGPT promotes healthcare: current applications and potential challenges – correspondence

Li,

Bao,

Yang

et al. 2024

International Journal of Surgery

View full text Add to dashboard Cite

Generative artificial intelligence in mental health care: potential benefits and current challenges

Cited by 8 publications

References 9 publications

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders

ChatGPT promotes healthcare: current applications and potential challenges – correspondence

Contact Info

Product

Resources

About