Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review.

Objectives: The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.

Materials And Methods: The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence.

Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review.

Background: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI.

Representing Sex and Gender Information in Biomedical Research.

In medicine and biomedical research, sex- and gender-related aspects are ubiquitous. If not considered adequately, a lower quality of research data can be expected together with a lower generalizability of study results with real-world settings. From a translational perspective, a lack of sex- and gender-sensitivity in acquired data can have negative implications for diagnosis, treatment (outcome and side effects), and risk prediction.