Critical analysis of information provided by ChatGPT on lactate, exercise, fatigue, and muscle pain: current insights and future prospects for enhancement
This study aimed to critically evaluate the information provided by ChatGPT on the role of lactate in fatigue and muscle pain during physical exercise. We inserted the prompt "What is the cause of fatigue and pain during exercise?" using ChatGPT versions 3.5 and 4o. In both versions, ChatGPT associated muscle fatigue with glycogen depletion and "lactic acid" accumulation, whereas pain was linked to processes such as inflammation and microtrauma. We deepened the investigation with ChatGPT 3.5, implementing user feedback to question the accuracy of the information about lactate. The response was then reformulated, involving a scientific debate about the true role of lactate in physical exercise and debunking the idea that it is the primary cause of muscle fatigue and pain. We also utilized the creation of a "well-crafted prompt," which included persona identification and thematic characterization, resulting in much more accurate information in both the ChatGPT 3.5 and 4o models, presenting a range of information from the physiological process of lactate to its true role in physical exercise. The results indicated that the accuracy of the responses provided by ChatGPT can vary depending on the data available in its database and, more importantly, on how the question is formulated. Therefore, it is indispensable that educators guide their students in the processes of managing the AI tool to mitigate risks of misinformation.<b>NEW & NOTEWORTHY</b> Generative artificial intelligence (AI), exemplified by ChatGPT, provides immediate and easily accessible answers about lactate and exercise. However, the reliability of this information may fluctuate, contingent upon the scope and intricacy of the knowledge derived from the training process before most recent update. Furthermore, a deep understanding of the basic principles of human physiology becomes crucial for the effective correction and safe use of this technology.
Authors
- Fernanda Patti Nakamoto
- Katja Weiss (ORCID: https://orcid.org/0000-0003-1247-6754)
- Marília Santos Andrade (ORCID: https://orcid.org/0000-0002-7004-4565)
- Ricardo Borges Viana (ORCID: https://orcid.org/0000-0001-9200-3185)
- Claudio André Barbosa de Lira (ORCID: https://orcid.org/0000-0001-5749-6877)
- Thomas Johannes Rosemann (ORCID: https://orcid.org/0000-0002-6436-6306)
- Rízia Rocha-Silva
- Rodrigo Luiz Vancini (ORCID: https://orcid.org/0000-0003-1981-1092)
- Beat Knechtle
- Mila Alves Matos Rodrigues
Institutions
- University of Zurich (CH)
- University of St.Gallen (CH)
- Universidade Federal do Ceará (BR)
- Medbase (CH)
- Centro Universitário São Camilo (BR)
- Universidade Federal do Espírito Santo (BR)
- Universidade Federal de Goiás (BR)
- Universidade Federal de São Paulo (BR)
Publication Details
- Journal
- AJP Advances in Physiology Education
- Published
- 2024-09-12
- DOI
- https://doi.org/10.1152/advan.00073.2024
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.36
Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico