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A foundation for evaluating the surgical artificial intelligence literature.
Daniel A Hashimoto1, Sai Koushik Sambasastry2, Vivek Singh3
1Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Global Surgical AI Collaborative, Toronto, ON, USA.
This guide helps surgeons understand artificial intelligence (AI) in surgery. It provides a framework for critically evaluating AI research, ensuring safe and effective application in surgical practice.
Area of Science:
- Medical Informatics
- Surgical Technology
- Artificial Intelligence in Medicine
Background:
- The increasing integration of artificial intelligence (AI) into surgical applications necessitates a specialized understanding for surgeons.
- Surgeons require foundational knowledge to critically assess AI-related scientific literature, product claims, and regulatory landscapes.
Purpose of the Study:
- To provide surgeons with a comprehensive framework for evaluating scientific manuscripts that utilize AI.
- To equip surgeons with the necessary tools to appraise the quality and applicability of AI in surgical practice.
Main Methods:
- The guide offers a glossary of common artificial intelligence terms.
- It includes an overview of prerequisite knowledge for understanding AI methodologies.
- Recommendations are provided for evaluating key manuscript elements, including data quality, methodology, and reproducibility.
Main Results:
- The framework enables surgeons to assess the quality of training data for AI algorithms.
- It facilitates evaluation of the appropriateness of methodological approaches in AI research.
- The guide aids in assessing the potential for reproducibility and applicability of AI in surgical settings.
Conclusions:
- This resource empowers surgeons to critically appraise AI in surgical literature and practice.
- It addresses considerations for generalizability and scalability of AI solutions in surgery.
- Enhanced understanding promotes the responsible and effective adoption of AI in surgical fields.
