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Artificial-intelligence systems can assist physicians with imaging interpretation, risk prediction and clinical decision support, but evidence from implementation research shows that performance in controlled datasets does not automatically translate to better patient care. Model drift, differences between hospitals, missing data and alert fatigue can reduce real-world utility. For internists, the key design principle is human-in-the-loop care: AI should surface patterns or prioritize attention while clinicians verify the result against the patient's history, examination and treatment context. Monitoring is also necessary after deployment to detect changes in accuracy and unexpected biases. This is particularly important in high-stakes situations such as sepsis, chest pain or medication decisions. The strongest use cases are those in which AI addresses a specific bottleneck and its output can be independently checked. APICON's focus on “AI versus clinical judgment” is therefore best framed as augmentation rather than replacement: the future system should make clinicians faster and more consistent without transferring accountability away from the medical team.
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Artificial-intelligence systems can assist physicians with imaging interpretation, risk prediction and clinical decision support, but evidence from implementation research shows that performance in controlled datasets does not automatically translate to better patient care. Model drift, differences between hospitals, missing data and alert fatigue can reduce real-world utility. For internists, the key design principle is human-in-the-loop care: AI should surface patterns or prioritize attention while clinicians verify the result against the patient's history, examination and treatment context. Monitoring is also necessary after deployment to detect changes in accuracy and unexpected biases. This is particularly important in high-stakes situations such as sepsis, chest pain or medication decisions. The strongest use cases are those in which AI addresses a specific bottleneck and its output can be independently checked. APICON's focus on “AI versus clinical judgment” is therefore best framed as augmentation rather than replacement: the future system should make clinicians faster and more consistent without transferring accountability away from the medical team.
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