The article explores the challenges and limitations of applying artificial intelligence (AI) in healthcare and the steps needed to translate AI technologies from research to clinical practice. Key challenges include the scientific nature of machine learning, logistical difficulties in implementation, and sociocultural barriers to adoption. Robust peer-reviewed clinical evaluations, using clinically applicable performance metrics, are essential for ensuring the safety and effectiveness of AI systems. The article emphasizes the need for independent, representative test sets to enable fair comparisons of different AI algorithms. Developers must address issues such as dataset shift, accidental fitting of confounders, and unintended discriminatory bias. Additionally, regulatory frameworks must balance the pace of innovation with potential risks, and post-market surveillance is crucial to monitor patient outcomes. The article concludes that achieving these goals will require further research in algorithm interpretability, human-Algorithm interaction, and the development of thoughtful regulatory policies.The article explores the challenges and limitations of applying artificial intelligence (AI) in healthcare and the steps needed to translate AI technologies from research to clinical practice. Key challenges include the scientific nature of machine learning, logistical difficulties in implementation, and sociocultural barriers to adoption. Robust peer-reviewed clinical evaluations, using clinically applicable performance metrics, are essential for ensuring the safety and effectiveness of AI systems. The article emphasizes the need for independent, representative test sets to enable fair comparisons of different AI algorithms. Developers must address issues such as dataset shift, accidental fitting of confounders, and unintended discriminatory bias. Additionally, regulatory frameworks must balance the pace of innovation with potential risks, and post-market surveillance is crucial to monitor patient outcomes. The article concludes that achieving these goals will require further research in algorithm interpretability, human-Algorithm interaction, and the development of thoughtful regulatory policies.