How can artificial intelligence be used to enhance education, clinical decision‑making, workflow efficiency, and research productivity for internal medicine residents, and what are its limitations?

Utility of Artificial Intelligence in Internal Medicine Residency

Internal medicine residents should integrate AI as a clinical decision support tool that augments—not replaces—clinical judgment, with focused training on appropriate use, interpretation of results, and recognition of algorithmic limitations to improve diagnostic accuracy and workflow efficiency. 1

Educational Framework for AI Integration

Core Competencies Required

Residents must develop AI literacy at two critical levels 1:

• First level: Ability to identify when an AI technology is appropriate for a specific clinical scenario and understand what inputs are required 1
• Second level: Capability to interpret AI-generated results in the context of errors and biases that may limit applicability for specific patient populations 1
• Training should take the form of progressively incremental data science education through statistical courses during residency or continuing education 1

Critical caveat: The context-specific nature of AI means performance of a given application may not be transferable across different clinical settings or patient populations 1

Curriculum Development Approach

• AI should be incorporated as a longitudinal thread throughout existing subjects rather than as isolated modules 2, 3
• Residents need to understand the breadth of AI tools, the framework for engineering AI solutions to clinical issues, and the role of data in AI development 2
• Case studies should include AI recommendations that present critical decision-making challenges to develop judgment skills 2
• Ethical implications of AI in medicine must be at the forefront of comprehensive medical education 2, 3

Clinical Decision-Making Applications

Diagnostic Enhancement

AI demonstrates strongest near-term utility in specific clinical domains 1:

• Medical imaging and digital pathology: Automated segmentation, volumetric analysis, ejection fraction calculation, and automated disease detection 4
• Risk stratification: Identification of patients at risk for near-term emergency room visits, prediction of mortality in immunotherapy, and early identification of patients who could benefit from specific treatments 1
• Pattern recognition: AI can identify patterns not discernible by humans, such as predicting genetic mutations from histopathology slides 1

Integration Requirements

• AI analytics must be presented through intuitive and interpretable human-computer interfaces that enhance user trust and integrate with existing clinical workflows 1
• Evaluation metrics should focus on quality of care and patient outcomes rather than technical performance of the model 1
• Critical limitation: At present, there remains a paucity of evidence that AI can positively affect patient outcomes compared with current standards of care 1

Workflow Efficiency Improvements

Near-Term Benefits

• Clinical operations: Quality improvement through risk stratification and patient identification 1
• Administrative tasks: AI-driven systems can streamline documentation and administrative workflows 5
• Natural language processing: Mid-term benefits expected for electronic health record analysis and research applications 1

Learning Curve Considerations

• Performance should be analyzed by graphically plotting user performance against experience, providing specific metrics for assessing resident competency development with AI tools 4
• Both "live evaluation" (affecting patient care) and "shadow mode" (not affecting care) should be distinguished, with implications for appropriate learner involvement at different training stages 4

Research Productivity Enhancement

Data Analysis Capabilities

• AI excels at analyzing complex medical datasets, identifying patterns, and extracting meaningful insights that might be missed by traditional analytical approaches 6
• Machine learning and deep learning can process vast amounts of data from electronic health records, imaging studies, and genetic information to generate new hypotheses 6
• Predictive models can forecast patient outcomes, treatment responses, and disease progression 6

Research Development Framework

• AI development should incorporate patient-centered outcomes research (PCOR) principles to ensure tools address meaningful clinical questions 4, 6
• Multidisciplinary teams including bioinformatics experts, medical specialists, and patient representatives should develop AI tools, providing diverse learning opportunities for trainees 4, 6

Critical Limitations and Pitfalls

Performance and Validation Issues

• Algorithm degradation: AI system performance may degrade over time as patient demographics, clinical context, or other factors change, requiring updates and reevaluation 1
• External validation failures: Proprietary models implemented in hundreds of hospitals have shown substantially worse performance than vendor-reported metrics, highlighting the need for external validation before adoption 1
• The "AI chasm": Few AI tools have demonstrated real benefit to patient care despite promising preclinical performance 4

Data Quality and Bias Concerns

• AI effectiveness heavily depends on data quality, with challenges including data annotation, storage, security, and standardization across different healthcare systems 6
• Algorithmic bias can create health disparities and must be actively identified and mitigated 1, 6, 5
• The "black box" nature of some algorithms presents obstacles to effective integration and trust 7, 5

Ethical and Privacy Considerations

• Data privacy concerns, transparency requirements, and fairness issues must be addressed 6, 5
• Accountability for AI errors remains an unresolved ethical dilemma 7
• Risk of provider dependency leading to disuse atrophy of clinical skills 7

Implementation Barriers

• Lack of faculty expertise in AI teaching 2
• Absence of standardized guidance on AI in medical education curricula 2
• Resistance from clinicians regarding interpretability and trust 5
• Reimbursement models must be developed to ensure wide access and avoid widening health care disparities 1

Evaluation and Reporting Standards

Quality Assessment Framework

• The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items and 10 generic items for early-stage clinical evaluation of AI decision support systems 4
• Early-stage clinical evaluation is critical to assess actual clinical performance at small scale, ensure safety, evaluate human factors, and pave the way for larger trials 4
• AI systems must account for complex intervention nature, user variability, human-computer collaboration, and changing system versions 4

Ongoing Monitoring Requirements

• AI tools require continuous monitoring and recalibration as new clinical information and research emerges 6
• Efficacy of AI algorithms should be "labeled" with precise descriptions of the subject population and intended clinical scenarios for use, similar to FDA drug labeling 1

Common pitfall to avoid: Using "plug and play" models without considering clinical relevance of predictions, workflow integration, or need for training and change management 1