Evaluating the Accuracy and Completeness of Artificial Intelligence Responses Against Established Otology Guidelines

Background: The incorporation of artificial intelligence (AI), especially large language models like Generative Pretrained Transformer 4 (GPT-4), into medical practice is a burgeoning field of interest. This research evaluates the applicability of GPT-4 in otology by analyzing its responses to queries based on otologic clinical practice guidelines. Methods: Key guidelines from otology were selected, and corresponding questions were formulated to examine GPT-4’s interpretation and response accuracy. Two independent reviewers assessed the AI-generated answers for accuracy and completeness, using a structured Likert scale. A re-evaluation was conducted to evaluate the reproducibility of the results. Results: The analysis showed a high accuracy level (mean score: 4.75 of 5) and completeness (mean score: 2.88 of 3) in GPT-4’s responses. The interrater agreement, as indicated by Cohen κ, was substantial. GPT-4 consistently advised on individualized treatment plans and professional consultation, particularly for guidelines with weaker evidence, demonstrating its cautious approach to handling medical information. Conclusion: GPT-4 exhibits promising potential as an auxiliary tool in otology, providing accurate and comprehensive information. However, its role should be viewed as supplementary, with emphasis on continual updates and careful monitoring to align with evolving medical knowledge. Future studies are recommended to further explore the depth of AI application in diverse clinical scenarios and its real-time impact on clinical outcomes.

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Publication Details

Journal
Otology & Neurotology Open
Published
2024-08-12
DOI
https://doi.org/10.1097/ono.0000000000000059
Citations
2
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.24
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article

Evaluating the Accuracy and Completeness of Artificial Intelligence Responses Against Established Otology Guidelines

Dayton L. Young, Kassandra K Corona, Yuki Yoshiyasu, Brian J. McKinnon et al.
2 citations
Otology & Neurotology Open
Artificial Intelligence in Healthcare and Education
0.24
article

Evaluating the Accuracy and Completeness of Artificial Intelligence Responses Against Established Otology Guidelines

Dayton L. Young, Kassandra K Corona, Yuki Yoshiyasu, Brian J. McKinnon, Nicholas A. Rossi
article en
2 citations

Abstract

Background: The incorporation of artificial intelligence (AI), especially large language models like Generative Pretrained Transformer 4 (GPT-4), into medical practice is a burgeoning field of interest. This research evaluates the applicability of GPT-4 in otology by analyzing its responses to queries based on otologic clinical practice guidelines. Methods: Key guidelines from otology were selected, and corresponding questions were formulated to examine GPT-4’s interpretation and response accuracy. Two independent reviewers assessed the AI-generated answers for accuracy and completeness, using a structured Likert scale. A re-evaluation was conducted to evaluate the reproducibility of the results. Results: The analysis showed a high accuracy level (mean score: 4.75 of 5) and completeness (mean score: 2.88 of 3) in GPT-4’s responses. The interrater agreement, as indicated by Cohen κ, was substantial. GPT-4 consistently advised on individualized treatment plans and professional consultation, particularly for guidelines with weaker evidence, demonstrating its cautious approach to handling medical information. Conclusion: GPT-4 exhibits promising potential as an auxiliary tool in otology, providing accurate and comprehensive information. However, its role should be viewed as supplementary, with emphasis on continual updates and careful monitoring to align with evolving medical knowledge. Future studies are recommended to further explore the depth of AI application in diverse clinical scenarios and its real-time impact on clinical outcomes.

Otology & Neurotology OpenVol. 4(3)
The University of Texas Medical Branch at Galveston (US)
Openalex Percentile: Top 41%
Artificial Intelligence in Healthcare and Education
0.24
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Evaluating the Accuracy and Completeness of Artificial Intelligence Responses Against Established Otology Guidelines — Dayton L. Young, Kassandra K Corona, et al. · Otology & Neurotology Open (2024) | TGRS Research Map | TGRS