Advancing clinical utility of artificial intelligence: lessons from developing a model to predict cochlear implant eligibility.

Objectives: To address the gap between artificial intelligence (AI) model development and clinical implementation by developing a machine-learning model to predict cochlear implant (CI) eligibility based on routine hearing tests, while illustrating 3 practical lessons: reformulation for clinical utility, selection of problem-specific metrics, and handling input data variability. Materials and Methods: Data were extracted from adult patients who underwent behavioral audiometric and subsequent CI candidacy testing (Arizona Biomedical and Consonant-Nucleus-Consonant tests) at Mayo Clinic between 2011 and 2023. Regression models were initially developed to predict AzBio and CNC scores using features from routine audiograms. Models were then reformulated into binary classification tasks using clinical thresholds (AzBio ≤60%; CNC <50%) and reported sensitivity and positive predictive value (PPV). Input data variability was assessed by analyzing test-retest differences in isophonemes scores. Results: Binary classification models achieved sensitivity of 90.4% with PPV 80.2% at CNC 50/AzBio 60, outperforming referral heuristics (60/60 and 75/40). Applying the models to 50 700 historical audiograms identified 525 patients at PPV 90% (sensitivity 58%), 396 at PPV 94% (sensitivity 44%), and 1582 at PPV 71% (sensitivity 94%). Variability analyses indicated that attainable precision is bounded by noise in key predictors. Conclusion: This study demonstrates how AI models can be translated into clinically actionable tools by reformulating regression outputs, selecting metrics aligned with clinical priorities, and addressing data variability. These strategies provide a scalable framework for implementing AI in health care and improving decision-making in diverse clinical contexts.