To chat or not to chat: how osteopathic students utilize artificial intelligence platforms in pharmacology education
- 1First Year Osteopathic Medical Student, Baptist University College of Osteopathic Medicine
- 2Pharmacology Faculty, Baptist University College of Osteopathic Medicine
Abstract
Introduction: The rapid development of artificial intelligence (AI) tools has led to their expanding use in academia, medicine, and medical education. However, the quality, accuracy, source reliability, and integration of osteopathic principles in AI‐generated content for pharmacology education remain insufficiently explored. Our goals were to use AI tools to generate pharmacology content, which we could critically evaluate for quality and accuracy, while exploring the capabilities and limitations of the outputs. The content we asked AI to generate was based on pharmacologically important concepts for osteopathic physicians in training, with the application of osteopathic philosophy included in some of the queries.
Methods: Three leading AI platforms, namely, Consensus Deep, Perplexity Pro, and ChatGPT Plus, were evaluated using three pharmacologically relevant queries. Each query was input into each platform, and the outputs were evaluated for quality, credibility, and source validity. The AI platforms we used required a paid subscription.
Results: Our results showed that Consensus Deep consistently provided output that included peer-reviewed journals and integrated osteopathic principles into its responses. Perplexity Pro provided information with references; several references included websites or social media posts, and when the prompt was refined and repeated, the output was limited and required additional purchase for further usage. ChatGPT Plus provided output which was patient-centered; when prompted for references, it provided references which had to be assessed for quality or credibility.
Discussion: Our study results indicate that generative AI platforms are a very useful tool for pharmacology and osteopathic medical education, although the use of the AI-generated content requires learners to critically evaluate the outputs for integrity, quality, and accuracy. In addition, there are currently limited data across AI platforms that include osteopathic principles and philosophy in pharmacology and therapeutics, and future work should be directed toward increasing scholarly research and publications in the fields of osteopathic medicine, pharmacology learning, and AI use.
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BibTeX
@article{barr2026chat,
title = {{To chat or not to chat: how osteopathic students utilize artificial intelligence platforms in pharmacology education}},
author = {Lyla Barr and Sammy Bakeer and Brooke Bemowski and Sarena Orio and Naunihal Zaveri},
journal = {Frontiers in Pharmacology},
year = {2026},
month = oct,
doi = {10.3389/fphar.2026.1930362},
url = {https://www.frontiersin.org/articles/10.3389/fphar.2026.1930362/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - To chat or not to chat: how osteopathic students utilize artificial intelligence platforms in pharmacology education
AU - Lyla Barr
AU - Sammy Bakeer
AU - Brooke Bemowski
AU - Sarena Orio
AU - Naunihal Zaveri
PY - 2026
DA - 2026/10/27
JO - Frontiers in Pharmacology
DO - 10.3389/fphar.2026.1930362
UR - https://www.frontiersin.org/articles/10.3389/fphar.2026.1930362/full
AB - Introduction: The rapid development of artificial intelligence (AI) tools has led to their expanding use in academia, medicine, and medical education. However, the quality, accuracy, source reliability, and integration of osteopathic principles in AI‐generated content for pharmacology education remain insufficiently explored. Our goals were to use AI tools to generate pharmacology content, which we could critically evaluate for quality and accuracy, while exploring the capabilities and limitations of the outputs. The content we asked AI to generate was based on pharmacologically important concepts for osteopathic physicians in training, with the application of osteopathic philosophy included in some of the queries. Methods: Three leading AI platforms, namely, Consensus Deep, Perplexity Pro, and ChatGPT Plus, were evaluated using three pharmacologically relevant queries. Each query was input into each platform, and the outputs were evaluated for quality, credibility, and source validity. The AI platforms we used required a paid subscription. Results: Our results showed that Consensus Deep consistently provided output that included peer-reviewed journals and integrated osteopathic principles into its responses. Perplexity Pro provided information with references; several references included websites or social media posts, and when the prompt was refined and repeated, the output was limited and required additional purchase for further usage. ChatGPT Plus provided output which was patient-centered; when prompted for references, it provided references which had to be assessed for quality or credibility. Discussion: Our study results indicate that generative AI platforms are a very useful tool for pharmacology and osteopathic medical education, although the use of the AI-generated content requires learners to critically evaluate the outputs for integrity, quality, and accuracy. In addition, there are currently limited data across AI platforms that include osteopathic principles and philosophy in pharmacology and therapeutics, and future work should be directed toward increasing scholarly research and publications in the fields of osteopathic medicine, pharmacology learning, and AI use.
SN - 1663-9812
ER -