Global Mental Health Services and the Impact of Artificial Intelligence–Powered Large Language Models

Heerden, Alastair C. van; Pozuelo, Julia Ruiz; Kohrt, Brandon A.

doi:10.1001/jamapsychiatry.2023.1253

Cited by 25 publications

(11 citation statements)

References 9 publications

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“…In partnership with other departments and functions including EAP, benefits, work/life, health promotion, HR, learning and development, employee relations, security, and others, the CMD can initiate and contribute to company programs, policies, and services that decrease the direct and indirect costs associated with MH/SA disorders. Lack of access to trained mental health providers is a global issue demanding a transformation in the way such care is delivered, which may include artificial intelligence models 76,77 …”

Section: Mental Healthmentioning

confidence: 99%

“…Lack of access to trained mental health providers is a global issue demanding a transformation in the way such care is delivered, which may include artificial intelligence models. 76,77 The application of artificial and augmented intelligence (AI) promises improvements in both clinician decision making and in which individuals receive timely, accurate, and contextualized mental health care. At the same time, factors such as uncertainty in the robustness of AI and machine learning models, and demographic biases in algorithms, demand vigilance, and advice from the CMD in a workplace setting to ensure the risks are highlighted and managed as the technology advances.…”

Section: Mental Healthmentioning

confidence: 99%

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Role and Value of the Corporate Medical Director

Stave,

Burton,

Heron

et al. 2023

J Occup Environ Med

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The role of the Corporate Medical Director (CMD) has received increased attention during the COVID-19 pandemic and has continued to evolve. This updated guidance addresses the role and value of the CMD in: health policy, strategy, and leadership; fostering a culture of health and well-being; supporting worker health and productivity/performance; addressing mental health; collaborating on employer benefits design; developing programs for global health, travel medicine, and remote/extreme environments; overseeing on- and near-site clinics; incorporating digital technology, artificial intelligence, and telehealth in health programs; supporting critical incident preparedness and business continuity planning; addressing workplace hazards; and overseeing periodic executive health examinations.

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Section: Mental Healthmentioning

confidence: 99%

Section: Mental Healthmentioning

confidence: 99%

Role and Value of the Corporate Medical Director

Stave,

Burton,

Heron

et al. 2023

J Occup Environ Med

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“…These factors contribute to a treatment gap, leaving a substantial portion of the population without proper support. To address these challenges, technological advancements, including artificial intelligence (AI), have paved the way for innovative solutions in mental health care ( 5 ), with the potential to augment existing services and bridge treatment gaps ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

ChatGPT is not ready yet for use in providing mental health assessment and interventions

Dergaa,

Fekih-Romdhane,

Hallit

et al. 2024

Front. Psychiatry

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BackgroundPsychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With advancements in technology and the rise of artificial intelligence (AI), there has been a growing interest in exploring the potential of AI language models systems, such as Chat Generative Pre-training Transformer (ChatGPT), to assist in the field of psychiatry.ObjectiveOur study aimed to evaluates the effectiveness, reliability and safeness of ChatGPT in assisting patients with mental health problems, and to assess its potential as a collaborative tool for mental health professionals through a simulated interaction with three distinct imaginary patients.MethodsThree imaginary patient scenarios (cases A, B, and C) were created, representing different mental health problems. All three patients present with, and seek to eliminate, the same chief complaint (i.e., difficulty falling asleep and waking up frequently during the night in the last 2°weeks). ChatGPT was engaged as a virtual psychiatric assistant to provide responses and treatment recommendations.ResultsIn case A, the recommendations were relatively appropriate (albeit non-specific), and could potentially be beneficial for both users and clinicians. However, as complexity of clinical cases increased (cases B and C), the information and recommendations generated by ChatGPT became inappropriate, even dangerous; and the limitations of the program became more glaring. The main strengths of ChatGPT lie in its ability to provide quick responses to user queries and to simulate empathy. One notable limitation is ChatGPT inability to interact with users to collect further information relevant to the diagnosis and management of a patient’s clinical condition. Another serious limitation is ChatGPT inability to use critical thinking and clinical judgment to drive patient’s management.ConclusionAs for July 2023, ChatGPT failed to give the simple medical advice given certain clinical scenarios. This supports that the quality of ChatGPT-generated content is still far from being a guide for users and professionals to provide accurate mental health information. It remains, therefore, premature to conclude on the usefulness and safety of ChatGPT in mental health practice.

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“…While LLMs hold potential, they present difficulties in understanding context and obtaining clarifications [42][43][44][45][46][47] . Addressing real-world medical issues requires handling multiple data modalities and must also provide authenticity, authority, accessibility, safety, empathy, and a human touch 48 .…”

Section: Introductionmentioning

confidence: 99%

Real-World Performance of Large Language Models in Emergency Department Chest Pain Triage

Meng,

Ji,

Yan

et al. 2024

Preprint

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Background: Large Language Models (LLMs) are increasingly being explored for medical applications, particularly in emergency triage where rapid and accurate decision-making is crucial. This study evaluates the diagnostic performance of two prominent Chinese LLMs, "Tongyi Qianwen" and "Lingyi Zhihui," alongside a newly developed model, MediGuide-14B, comparing their effectiveness with human medical experts in emergency chest pain triage. Methods: Conducted at Peking University Third Hospital's emergency centers from June 2021 to May 2023, this retrospective study involved 11,428 patients with chest pain symptoms. Data were extracted from electronic medical records, excluding diagnostic test results, and used to assess the models and human experts in a double-blind setup. The models' performances were evaluated based on their accuracy, sensitivity, and specificity in diagnosing Acute Coronary Syndrome (ACS). Findings: "Lingyi Zhihui" demonstrated a diagnostic accuracy of 76.40%, sensitivity of 90.99%, and specificity of 70.15%. "Tongyi Qianwen" showed an accuracy of 61.11%, sensitivity of 91.67%, and specificity of 47.95%. MediGuide-14B outperformed these models with an accuracy of 84.52%, showcasing high sensitivity and commendable specificity. Human experts achieved higher accuracy (86.37%) and specificity (89.26%) but lower sensitivity compared to the LLMs. The study also highlighted the potential of LLMs to provide rapid triage decisions, significantly faster than human experts, though with varying degrees of reliability and completeness in their recommendations. Interpretation: The study confirms the potential of LLMs in enhancing emergency medical diagnostics, particularly in settings with limited resources. MediGuide-14B, with its tailored training for medical applications, demonstrates considerable promise for clinical integration. However, the variability in performance underscores the need for further fine-tuning and contextual adaptation to improve reliability and efficacy in medical applications. Future research should focus on optimizing LLMs for specific medical tasks and integrating them with conventional medical systems to leverage their full potential in real-world settings.

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Global Mental Health Services and the Impact of Artificial Intelligence–Powered Large Language Models

Abstract: This Viewpoint describes ways in which artificial intelligence–powered large language models may be used to improve the delivery of mental health services worldwide.

Cited by 25 publications

References 9 publications

Role and Value of the Corporate Medical Director

Role and Value of the Corporate Medical Director

ChatGPT is not ready yet for use in providing mental health assessment and interventions

Real-World Performance of Large Language Models in Emergency Department Chest Pain Triage

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