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STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence

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Summary
This summary is machine-generated.

Developing standardized guidelines is essential for evaluating generative artificial intelligence (AI) in medicine. This study introduces a comprehensive framework and checklist to ensure reliable assessment of AI tools in healthcare.

Keywords:
generative AImedical and life science contextsreliabilitystandardized assessment guidelines

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Healthcare Technology

Background:

  • Generative artificial intelligence (AI) shows promise in medicine, but its implementation is hampered by a lack of standardized evaluation frameworks and methodological issues in current research.
  • Reliable and consistent assessment of generative AI in healthcare necessitates standardized evaluation guidelines.

Purpose of the Study:

  • To develop robust, standardized guidelines for evaluating the performance of generative AI in medical applications.
  • To provide a systematic approach for assessing generative AI's utility and reliability in healthcare settings.

Main Methods:

  • A comprehensive literature review was conducted across Web of Science, Cochrane Library, PubMed, and Google Scholar, focusing on studies evaluating generative AI in medicine.
  • A multidisciplinary team of experts engaged in discussion sessions to formulate a detailed 32-item checklist.
  • The developed framework covers critical evaluation aspects, including question formulation, querying methods, and assessment techniques.

Main Results:

  • A comprehensive 32-item checklist and a broader assessment framework were created to guide the evaluation of generative AI in medical contexts.
  • The framework offers a clear pathway from initial question development to the final assessment of results.
  • It addresses potential challenges and enhances the quality and reporting of research involving generative AI in medicine.

Conclusions:

  • The developed framework provides a standardized and systematic method for testing the applicability of generative AI in medicine.
  • This approach aims to improve the quality of research and reporting, facilitating the advancement of generative AI in medical and life sciences.
  • The guidelines are crucial for ensuring the reliable and consistent evaluation of generative AI tools in healthcare.