ChatGPT and the Future of Digital Health: A Study on Healthcare Workers’ Perceptions and Expectations

Temsah, Mohamad-Hani; Aljamaan, Fadi; Malki, Khalid H.; Alhasan, Khalid; Altamimi, Ibraheem; Aljarbou, Razan; Bazuhair, Faisal; Alsubaihin, Abdulmajeed; Abdulmajeed, Naif; Alshahrani, Fatimah S.; Temsah, Reem; Alshahrani, Turki; Al-Eyadhy, Lama; Alkhateeb, Serin Mohammed; Saddik, Basema; Halwani, Rabih; Jamal, Amr; Al-Tawfiq, Jaffar A.; Al-Eyadhy, Ayman

doi:10.3390/healthcare11131812

Cited by 76 publications

(38 citation statements)

References 39 publications

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“…In a survey that evaluated healthcare professionals’ experiences with ChatGPT, a substantial majority (75.1%) indicated that they felt at ease with the prospect of incorporating ChatGPT into their healthcare routines, with 39.5% favouring using ChatGPT to assist in making medical decisions. 42 However, it must be emphasised that while LLMs can serve as valuable tools, they should complement rather than replace the expertise of qualified mental health professionals to ensure comprehensive and personalised care.…”

Section: Discussionmentioning

confidence: 99%

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Elyoseph,

Levkovich,

Shinan-Altman

2024

Fam Med Com Health

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BackgroundArtificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI’s role in evaluating prognosis and long-term outcomes in depressive disorders, offering insights into how AI large language models (LLMs) compare with human perspectives.MethodsUsing case vignettes, we conducted a comparative analysis involving different LLMs (ChatGPT-3.5, ChatGPT-4, Claude and Bard), mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses), and the general public that reported previously. We evaluate the LLMs ability to generate prognosis, anticipated outcomes with and without professional intervention, and envisioned long-term positive and negative consequences for individuals with depression.ResultsIn most of the examined cases, the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication. ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs, professionals and the public. ChatGPT-4, Claude and Bard aligned closely with mental health professionals and the general public perspectives, all of whom anticipated no improvement or worsening without professional help. Regarding long-term outcomes, ChatGPT 3.5, Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4.ConclusionsThis study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology’s prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients’ drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.

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Section: Discussionmentioning

confidence: 99%

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Elyoseph,

Levkovich,

Shinan-Altman

2024

Fam Med Com Health

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“…The integration of artificial intelligence and large language models in systematic reviews can also affect trust in health research. We anticipate that due to limited experience, evidence users will be more cautious about the application of studies that use such tools (45,46).…”

Section: Discussionmentioning

confidence: 99%

ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: A methods study

Pitre,

Jassal,

Talukdar

et al. 2023

Preprint

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BackgroundThe assessment of risk of bias is a critical component of systematic review methods. Assessing risk of bias, however, can be time- and resource-intensive. AI-based solutions may increase efficiency and reduce burden.ObjectiveTo evaluate the reliability of ChatGPT for performing risk of bias assessments of randomized trials.MethodsWe sampled recently published Cochrane systematic reviews of medical interventions (up to October 2023) that included randomized controlled trials and assessed risk of bias using the Cochrane-endorsed revised risk of bias tool for randomized trials (RoB 2.0). From each eligible review, we collected data on the risk of bias assessments for the first three reported outcomes. Using ChatGPT-4, we assessed the risk of bias for the same outcomes using three different prompts: a minimal prompt including limited instructions, a maximal prompt with extensive instructions, and an optimized prompt that was designed to yield the best risk of bias judgments. The agreement between ChatGPT’s assessments and those of the systematic reviewers was quantified using weighted kappa statistics.ResultsWe included 34 systematic reviews with 157 unique trials. We found the agreement between ChatGPT and systematic review authors for assessment of overall risk of bias to be 0.16 (95% CI: 0.01 to 0.3) for the maximal ChatGPT prompt, 0.17 (95% CI: 0.02 to 0.32) for the optimized prompt, and 0.11 (95% CI: -0.04 to 0.27) for the minimal prompt. For the optimized prompt, agreement ranged between 0.11 (95% CI: -0.11 to 0.33) to 0.29 (95% CI: 0.14 to 0.44) across risk of bias domains, with the lowest agreement for the deviations from the intended intervention domain and the highest agreement for the missing outcome data domain.ConclusionOur results suggest that ChatGPT and systematic reviewers only have “slight” to “fair” agreement in risk of bias judgments for randomized trials. ChatGPT is currently unable to reliably assess risk of bias of randomized trials. We recommend systematic reviewers avoid using ChatGPT to perform risk of bias assessments.

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“…OpenAI launched in last November 2022 the Chat Generative Pre-trained Transformer (ChatGPT) and revolutionised the approach in artificial intelligence to human–model interaction used for fields [ 4 ]. It was used in all areas: healthcare and management [ 5 , 6 , 7 , 8 , 9 ], aiding cosmetic orthognathic surgery consultation [ 10 ], revolutionising dental practices [ 8 , 11 , 12 ], medical scientific articles and education fields [ 6 , 9 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ], support in disease diagnosis [ 24 ], revolutionising radiology, sonar imaging and writing assessment [ 13 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], pharmaceutical research and treatment [ 11 , 24 , 34 , 35 , 36 , 37 , 38 ], and navigating limitations and ethical considerations [ 18 , 19 , 31 , 35 , 38 , 39 ,…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges

Younis,

Eisa,

Nasser

et al. 2024

Diagnostics

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Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.

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ChatGPT and the Future of Digital Health: A Study on Healthcare Workers’ Perceptions and Expectations

Cited by 76 publications

References 39 publications

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public

ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: A methods study

A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges

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