A Critical Review of ChatGPT as a Potential Substitute for Diabetes Educators

Sharma, Samriddhi; Pajai, Sandhya; Prasad, Roshan; Wanjari, Mayur B; Munjewar, Pratiksha K; Sharma, Ranjana; Pathade, Aniket

doi:10.7759/cureus.38380

Cited by 17 publications

(13 citation statements)

References 16 publications

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“…Some research also supported this opinion. ChatGPT had the potential to be used as a diabetic educator [23]. It could also provide an effective diet plan for people with food allergies, albeit with minor errors [13].…”

Section: Discussionmentioning

confidence: 99%

Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions

Tangadulrat,

Sono,

Tangtrakulwanich

2023

JMIR Med Educ

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Background ChatGPT is a well-known large language model–based chatbot. It could be used in the medical field in many aspects. However, some physicians are still unfamiliar with ChatGPT and are concerned about its benefits and risks. Objective We aim to evaluate the perception of physicians and medical students toward using ChatGPT in the medical field. Methods A web-based questionnaire was sent to medical students, interns, residents, and attending staff with questions regarding their perception toward using ChatGPT in clinical practice and medical education. Participants were also asked to rate their perception of ChatGPT’s generated response about knee osteoarthritis. Results Participants included 124 medical students, 46 interns, 37 residents, and 32 attending staff. After reading ChatGPT’s response, 132 of the 239 (55.2%) participants had a positive rating about using ChatGPT for clinical practice. The proportion of positive answers was significantly lower in graduated physicians (48/115, 42%) compared with medical students (84/124, 68%; P<.001). Participants listed a lack of a patient-specific treatment plan, updated evidence, and a language barrier as ChatGPT’s pitfalls. Regarding using ChatGPT for medical education, the proportion of positive responses was also significantly lower in graduate physicians (71/115, 62%) compared to medical students (103/124, 83.1%; P<.001). Participants were concerned that ChatGPT’s response was too superficial, might lack scientific evidence, and might need expert verification. Conclusions Medical students generally had a positive perception of using ChatGPT for guiding treatment and medical education, whereas graduated doctors were more cautious in this regard. Nonetheless, both medical students and graduated doctors positively perceived using ChatGPT for creating patient educational materials.

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Section: Discussionmentioning

confidence: 99%

Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions

Tangadulrat,

Sono,

Tangtrakulwanich

2023

JMIR Med Educ

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“…Those tasks represented a sample of proposed LLM uses that were synthesized from the literature and included, but were not limited to, optimizing alerts for clinical decision support, providing a differential diagnosis, writing a discharge summary, recommending treatment options, translating radiology reports into layperson language, writing scientific manuscripts, and generating personalized study plans for students or trainees among others. 2 6 7 8 15 16 17 18 19 20 21 22 23 24 25 …”

Section: Methodsmentioning

confidence: 99%

A Survey of Clinicians' Views of the Utility of Large Language Models

Spotnitz,

Idnay,

Gordon

et al. 2024

Appl Clin Inform

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Objective: Large language models (LLMs) like ChatGPT are powerful algorithms that have been shown to produce human-like text from input data. A number of potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed healthcare providers for their opinions about whether the technology is fit for use. Materials and Methods: We distributed a validated mixed-methods survey to gauge practicing clinicians’ comfort with LLMs for a breadth of tasks in clinical practice, research and education, which were selected from the literature. Results: A total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, healthcare providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias. Discussion: Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee. Conclusion: In a uniquely rigorous and comprehensive mixed-methods survey of clinicians about LLM use, healthcare providers were encouraging of having LLMs in healthcare for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence (AI) in general.

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“…Artificial intelligence (AI) natural language processing chatbots such as ChatGPT are freely accessible to the general public. In clinical medicine, investigators have studied the role of ChatGPT in training medical students and in educating patients on diabetes mellitus, highlighting several key strengths but also raising potential pitfalls and risks with its use [1][2][3]. More recently, we showed that ChatGPT can play a complementary role to a traveller's health consultation in providing pre-travel advice [4].…”

Section: Introductionmentioning

confidence: 99%

The role of an artificial intelligence model in antiretroviral therapy counselling and advice for people living with HIV

Koh,

Ngiam,

Yong

et al. 2024

HIV Medicine

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ObjectivesPeople living with HIV may find personalized access to accurate information on antiretroviral therapy (ART) challenging given the stigma and costs potentially associated with attending physical consultations. Artificial intelligence (AI) chatbots such as ChatGPT may help to lower barriers to accessing information addressing concerns around ART initiation. However, the safety and accuracy of the information provided remains to be studied.MethodsWe instructed ChatGPT to answer questions that people living with HIV frequently ask about ART, covering i) knowledge of and access to ART; ii) ART initiation, side effects, and adherence, and iii) general sexual health practices while receiving ART. We checked the accuracy of the advice against international HIV clinical practice guidelines.ResultsChatGPT answered all questions accurately and comprehensively. It recognized potentially life‐threatening scenarios such as abacavir hypersensitivity reaction and gave appropriate advice. However, in certain contexts, such as specific geographic locations or for pregnant individuals, the advice lacked specificity to an individual's unique circumstances and may be inadequate. Nevertheless, ChatGPT consistently re‐directed the individual to seek help from a healthcare professional to obtain targeted advice.ConclusionsChatGPT may act as a useful adjunct in the process of ART counselling for people living with HIV. Improving access to information on and knowledge about ART may improve access and adherence to ART and outcomes for people living with HIV overall.

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A Critical Review of ChatGPT as a Potential Substitute for Diabetes Educators

Cited by 17 publications

References 16 publications

Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions

Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions

A Survey of Clinicians' Views of the Utility of Large Language Models

The role of an artificial intelligence model in antiretroviral therapy counselling and advice for people living with HIV

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