Evidence Requirements for Clinical AI Adoption

Uses

Benefits

Evidence quality

Highlighted claims

• The ML-CDSS model must be trained and validated on patient data from diverse demographic backgrounds to be applicable to the UK's varied patient population. — What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy? A qualitative study
• Building clinician trust requires that clinicians can observe a tool's reliability and validity before integrating its outputs into clinical decisions. — What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy? A qualitative study
• Transparent reporting of AI development methods, appropriate use cases, and performance metrics is essential for credible clinical adoption. — What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy? A qualitative study
• Generating real-world evidence through clinical case studies of ML-CDSS use was identified as critical to broader adoption. — What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy? A qualitative study
• Developing implementation blueprints describing how the ML-CDSS could be embedded across different settings was proposed as a way to encourage wider adoption. — What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy? A qualitative study
• AI tools in healthcare should be framed as support for shared decision-making rather than as autonomous decision-makers, which can improve clinician-patient communication. — What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients’ risk of hepatic and renal deterioration during chemotherapy? A qualitative study