Artificial intelligence (AI) holds great promise for transforming healthcare, but translating AI research into clinical practice faces significant challenges. Key issues include the limitations of machine learning algorithms, logistical difficulties in implementation, and the need for sociocultural and pathway changes. Robust clinical evaluation through randomized controlled trials is essential, but such trials are rare. Performance metrics should reflect real-world clinical applicability and be understandable to users. Regulation must balance innovation with safety, and post-market surveillance is necessary to ensure patient safety. Independent, representative test sets are needed for fair algorithm comparisons. Developers must be vigilant about potential dangers like dataset shift, confounder fitting, and bias. AI systems must be interpretable and generalizable across populations. Challenges include ensuring fairness, avoiding unintended biases, and improving algorithmic interpretability. AI must be integrated into clinical workflows with consideration for human factors, including alert fatigue and human-AI interaction. Regulatory frameworks must evolve to ensure safe and effective AI deployment. Despite these challenges, AI has the potential to improve healthcare outcomes, but its success depends on addressing these issues through rigorous research, ethical development, and clinical validation.Artificial intelligence (AI) holds great promise for transforming healthcare, but translating AI research into clinical practice faces significant challenges. Key issues include the limitations of machine learning algorithms, logistical difficulties in implementation, and the need for sociocultural and pathway changes. Robust clinical evaluation through randomized controlled trials is essential, but such trials are rare. Performance metrics should reflect real-world clinical applicability and be understandable to users. Regulation must balance innovation with safety, and post-market surveillance is necessary to ensure patient safety. Independent, representative test sets are needed for fair algorithm comparisons. Developers must be vigilant about potential dangers like dataset shift, confounder fitting, and bias. AI systems must be interpretable and generalizable across populations. Challenges include ensuring fairness, avoiding unintended biases, and improving algorithmic interpretability. AI must be integrated into clinical workflows with consideration for human factors, including alert fatigue and human-AI interaction. Regulatory frameworks must evolve to ensure safe and effective AI deployment. Despite these challenges, AI has the potential to improve healthcare outcomes, but its success depends on addressing these issues through rigorous research, ethical development, and clinical validation.