A scoping review of silent trials for medical artificial intelligence

Source Domain: www.nature.com

Machine learning and its application in healthcare have led to a surge in research, but concerns about inflated expectations and implementation gaps in real-world settings have emerged.
Ethical integration of healthcare AI, along with the development of frameworks addressing both ethical and technical aspects in clinical translation, is crucial.
Challenges include the assessment of deployment costs, generalization of AI models to diverse populations, and ensuring the clinical efficacy and safety in real-world settings.
Successful deployment of AI models requires validation and regulatory compliance, transparency in model operations, and consideration of bias and fairness issues.
There is a need for structured reporting guidelines and frameworks to ease the transition from AI model development to clinical practice.
The integration of AI tools in healthcare must involve comprehensive evaluation methodologies, human factors, and consider sociotechnical systems to enhance usability and trust.
Effective translation of machine learning products into clinical use involves interdisciplinary collaboration, stakeholder engagement, and iterative development based on clinical feedback.

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