AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.3930
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ORIGINAL RESEARCH ARTICLE

An exploratory study on the potential of ChatGPT as an AI-assisted diagnostic tool for visceral leishmaniasis

Paulo Adriano Schwingel1,2,3,4†* ,  Dino Schwingel1,2† ,  Samuel Ricarte de Aquino1,5† ,  Aline Rafaela Soares da Silva1,2,3 ,  Pedro Paulo Ramos da Silva1,2 ,  Renato Augusto da Cruz Pereira1,2,6 ,  Daniela Conceição Gomes Gonçalves e Silva1,2,4 ,  Amanda Alves Marcelino da Silva1,2,3,4 ,  Flavia Emília Cavalcante Valença Fernandes1,2 ,  Maria Jacqueline Silva Ribeiro1,2,6 ,  Paulo Ditarso Maciel Júnior1,7 ,  Paulo Gustavo Serafim de Carvalho1,8 ,  Ricardo Kenji Shiosaki1,2 ,  Rogério Fabiano Gonçalves1,2 ,  Bruno Bavaresco Gambassi1,2,6 ,  Paula Andreatta Maduro1,2,4
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1 AI-assisted Diagnostics Research Group, Universidade de Pernambuco, Petrolina, Pernambuco, Brazil
2 Human Performance Research Laboratory, Universidade de Pernambuco, Petrolina, Pernambuco, Brazil
3 Postgraduate Program in Rehabilitation and Functional Performance, Universidade de Pernambuco, Petrolina, Pernambuco, Brazil
4 Postgraduate Program in Health Sciences, Universidade de Pernambuco, Recife, Pernambuco, Brazil
5 Dr. Washington Antônio de Barros Teaching Hospitalian Hospital Services Company, Petrolina, Pernambuco, Brazil
6 Postgraduate Program on Management and Health Programs and Services, CEUMA University, São Luís, Maranhão, Brazil
7 Postgraduate Program in Information Technology, Federal Institute of Paraíba, João Pessoa, Paraíba, Brazil
8 College of Agricultural and Environmental Sciences, Federal University of Vale do São Francisco, Juazeiro, Bahia, Brazil
AIH, 3930 https://doi.org/10.36922/aih.3930
Submitted: 13 June 2024 | Accepted: 20 September 2024 | Published: 16 October 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Visceral leishmaniasis (VL) is a severe parasitic disease that poses significant diagnostic challenges due to its complex presentation and the necessity for comprehensive diagnostic methods. This exploratory study investigates the potential of Chat Generative Pre-trained Transformer (ChatGPT)/GPT-4, an artificial intelligence (AI) chatbot, in assisting the diagnostic process for VL. We evaluated the diagnostic accuracy of ChatGPT/GPT-4 in generating differential diagnosis lists for eight clinical vignette cases of VL, authored by a Brazilian infectious disease doctor. Our findings reveal that ChatGPT/GPT-4 included VL in the top five differential diagnoses in 75% of the cases (95% confidence interval [CI]: 40.1 – 93.7%) and identified VL as the top diagnosis in 50% of the cases (95% CI: 30.3 – 86.5%). These results underscore the high potential of ChatGPT/GPT-4 as an AI-assisted diagnostic tool, which is capable of providing accurate differential diagnoses and assisting healthcare professionals in resource-limited settings. The study highlights the broader applicability of AI chatbots in medical diagnostics, not only for common conditions but also for specialized and less prevalent diseases like VL. By integrating AI tools into the diagnostic workflow, healthcare providers can enhance their diagnostic accuracy and efficiency, ultimately improving patient outcomes. This research contributes to the growing body of evidence supporting the utility of AI in healthcare and underscores the need for further studies to validate these findings across larger and more diverse clinical scenarios.

Keywords
Tropical neglected diseases
Artificial neural network
Differential diagnosis
Artificial intelligence-assisted diagnosis
Healthcare technology
Funding
This study received financial support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) under grant number 408003/2023-5 and from the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) under grant numbers APQ- 1413-4.08/21 and APQ-0238-4.01/24. Additionally, Paulo Adriano Schwingel was awarded a Research Productivity Grant (BPP) from the FACEPE under number BPP-0003- 4.01/24 and Daniela Conceição Gomes Gonçalves e Silva was awarded a Technical Cooperation Grant (BCT) from the FACEPE under number BCT-0355-4.08/23.
Conflict of interest
Paulo Adriano Schwingel is an editorial board member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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