Assessing the ChatGPT aptitude: A competent and effective Dermatology doctor?

Atopic dermatitis Autoimmune blistering skin diseases ChatGPT Evidence-based medicine

Journal

Heliyon
ISSN: 2405-8440
Titre abrégé: Heliyon
Pays: England
ID NLM: 101672560

Informations de publication

Date de publication:
15 Sep 2024
Historique:
received: 13 07 2023
revised: 29 08 2024
accepted: 29 08 2024
medline: 25 9 2024
pubmed: 25 9 2024
entrez: 25 9 2024
Statut: epublish

Résumé

The efficacy and adeptness of ChatGPT 3.5 and ChatGPT 4.0 in the precise diagnosis and management of conditions like atopic dermatitis and Autoimmune blistering skin diseases (AIBD) remain to be elucidated. So this study examined the accuracy and effectiveness of the ChatGPT responses related to understanding, therapies, and specific cases of these two conditions. Firstly, the responses provided by ChatGPTs to a set of 50 questionnaires underwent evaluation by five distinct dermatologists, with complete adjudication of the third-party reviewer. The comparative analysis included the evaluative efficacy of both ChatGPT3.5 and ChatGPT4.0 against the diagnostic abilities exhibited by three distinct cohorts of qualified clinical professionals. And then, an examination was conducted to assess the diagnostic proficiency of ChatGPT3.5 and ChatGPT4.0 in the context of diagnosing specific instances of skin blistering autoimmune diseases. In assessing the proficiency of ChatGPTs in generating responses related to fundamental knowledge about AD it is noteworthy that both versions of ChatGPTs, despite their lack of specialized training on medical databases, exhibited a commendable capacity to yield solutions that exhibited a substantial degree of concurrence with evidence-based medical information. Accordingly we observed that the performance of ChatGPT-4.0 beyond that of the ChatGPT-3.5. However, it it crucial to emphasize that ChatGPT-4.0 did not show the ability to offer answers surpassing those provided by associate senior, and senior medical professionals. In the assessment designed to determine the proficiency of ChatGPTs in recognizing particular type of AIBD, it is evident that both ChatGPT-4 and ChatGPT-3.5 demonstrated inadequacy in providing responses that are both precise and accurate for each individual occurrence of this skin condition. Both ChatGPT-3.5 and ChatGPT-4.0 satisfactory for addressing fundamental inquiries related to atopic dermatitis, however they prove insufficient for diagnosing AIBD. The progress of ChatGPT in achieving utility within the professional medical domain remains a considerable journey ahead.

Sections du résumé

Background UNASSIGNED
The efficacy and adeptness of ChatGPT 3.5 and ChatGPT 4.0 in the precise diagnosis and management of conditions like atopic dermatitis and Autoimmune blistering skin diseases (AIBD) remain to be elucidated. So this study examined the accuracy and effectiveness of the ChatGPT responses related to understanding, therapies, and specific cases of these two conditions.
Method UNASSIGNED
Firstly, the responses provided by ChatGPTs to a set of 50 questionnaires underwent evaluation by five distinct dermatologists, with complete adjudication of the third-party reviewer. The comparative analysis included the evaluative efficacy of both ChatGPT3.5 and ChatGPT4.0 against the diagnostic abilities exhibited by three distinct cohorts of qualified clinical professionals. And then, an examination was conducted to assess the diagnostic proficiency of ChatGPT3.5 and ChatGPT4.0 in the context of diagnosing specific instances of skin blistering autoimmune diseases.
Results UNASSIGNED
In assessing the proficiency of ChatGPTs in generating responses related to fundamental knowledge about AD it is noteworthy that both versions of ChatGPTs, despite their lack of specialized training on medical databases, exhibited a commendable capacity to yield solutions that exhibited a substantial degree of concurrence with evidence-based medical information. Accordingly we observed that the performance of ChatGPT-4.0 beyond that of the ChatGPT-3.5. However, it it crucial to emphasize that ChatGPT-4.0 did not show the ability to offer answers surpassing those provided by associate senior, and senior medical professionals. In the assessment designed to determine the proficiency of ChatGPTs in recognizing particular type of AIBD, it is evident that both ChatGPT-4 and ChatGPT-3.5 demonstrated inadequacy in providing responses that are both precise and accurate for each individual occurrence of this skin condition.
Conclusion UNASSIGNED
Both ChatGPT-3.5 and ChatGPT-4.0 satisfactory for addressing fundamental inquiries related to atopic dermatitis, however they prove insufficient for diagnosing AIBD. The progress of ChatGPT in achieving utility within the professional medical domain remains a considerable journey ahead.

Identifiants

pubmed: 39319150
doi: 10.1016/j.heliyon.2024.e37220
pii: S2405-8440(24)13251-3
pmc: PMC11419909
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e37220

Informations de copyright

© 2024 The Authors. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Chengliang yin, declare that he have no conflicts of interest related to the research, authors, or funding sources of the articles. He is handling as an AE of this journal. He maintain the highest standards of objectivity, fairness, and integrity in his evaluations and decisions. It is his responsibility to ensure that the articles the handle are evaluated and published based solely on their scientific merit and relevance to the journal's scope, without any undue influence or bias. He am committed to upholding the journal's standards of excellence and ethical conduct in all my work as an AE.

Auteurs

Chengxiang Lian (C)

Department of Dermatology and Venereology, The First Affiliated Hospital of Guang-xi Medical University, Nanning, 530021, China.

Xin Yuan (X)

Department of Dermatology, GuiZhou Provincial People's Hospital, Guiyang, 550000, China.

Santosh Chokkakula (S)

Department of Microbiology, Chungbuk National University College of Medicine and Medical Research Institute, Cheongju, Chungbuk, 28644, South Korea.

Guanqing Wang (G)

Department of Dermatology, Shanghai General Hospital (South), Shanghai Jiao Tong University, No. 650, New Songjiang Road, Shanghai, 200000, China.

Biao Song (B)

Zhihui Big Data Research Institute of Inner Mongolia, Inner Mongolia, 010020, China.
Collaborative Innovation Center of Big Data Application Research of Inner Mongolia University of Finance and Economics, Inner Mongolia, 010020, China.

Zhe Wang (Z)

Zhihui Big Data Research Institute of Inner Mongolia, Inner Mongolia, 010020, China.

Ge Fan (G)

Lightspeed & Quantum Studios, Tencent Inc., Shenzhen, 693388, China.

Chengliang Yin (C)

Faculty of Medicine, Macau University of Science and Technology, Macau, 999078, China.

Classifications MeSH