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Improving Clinical Decision-Making in Treating Airway Diseases With an Expert System Built Upon the Free AI Tool
Cheng-Hao Hsu1, Ching-Li Hsu2, Chih-Hsiang Tsou3,4
1Department of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Artificial intelligence (AI) tools can aid in diagnosing and managing airway diseases. This study shows Google NotebookLM® provides guideline-based recommendations, improving medical staff knowledge and potentially saving consultation time.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Clinical Decision Support Systems
Background:
- Developing effective clinical decision support tools is crucial for managing complex diseases.
- Artificial intelligence (AI) and large language models (LLMs) offer new possibilities for medical applications.
- Airway diseases require timely and accurate diagnosis and management, often benefiting from readily accessible guideline information.
Purpose of the Study:
- To develop and evaluate an AI-powered medical decision-making aid for airway diseases using Google NotebookLM®.
- To assess the AI system's functionality, performance, and impact on clinical workflow in an emergency department setting.
Main Methods:
- Utilized Google NotebookLM® (powered by Gemini 2.0 LLM) fed with clinical guidelines for airway diseases.
- Evaluated system feasibility, behavior, ability, and potential through simulated cases and expert review.
- Assessed accuracy and completeness of AI responses by three independent pulmonologists.
- Deployed the system in an emergency department for testing by medical staff (n=20) and collected feedback via questionnaires.
Main Results:
- 66.7% of specialist ratings for AI responses were above average, with moderate inter-rater reliability for accuracy (ICC=0.612) and good for completeness (ICC=0.773).
- The AI system provided reasonable answers in an emergency department setting, enhancing staff medical literacy.
- While not statistically significant across all participants, physician responses indicated a potential for time savings in consultations.
Conclusions:
- The AI system is customizable, cost-efficient, and accessible for clinicians without coding expertise for airway disease management.
- It offers reliable guideline-based recommendations, boosts medical knowledge, and may reduce physician consultation time.
- Further evaluation in diverse medical disciplines and healthcare settings is warranted.
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