Data-driven overdiagnosis definitions: A scoping review

Introduction: Adequate methods to promptly translate digital health innovations for improved patient care are essential. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have been sources of digital innovation and hold the promise to revolutionize the way we treat, manage and diagnose patients. Understanding the benefits but also the potential adverse effects of digital health innovations, particularly when these are made available or applied on healthier segments of the population is essential. One of such adverse effects is overdiagnosis. Objective: to comprehensively analyze quantification strategies and data-driven definitions for overdiagnosis reported in the literature. Methods: we conducted a scoping systematic review of manuscripts describing quantitative methods to estimate the proportion of overdiagnosed patients. Results: we identified 46 studies that met our inclusion criteria. They covered a variety of clinical conditions, primarily breast and prostate cancer. Methods to quantify overdiagnosis included both prospective and retrospective methods including randomized clinical trials, and simulations. Conclusion: a variety of methods to quantify overdiagnosis have been published, producing widely diverging results. A standard method to quantify overdiagnosis is needed to allow its mitigation during the rapidly increasing development of new digital diagnostic tools.

Automated summary

Adequate translation methods are essential for promptly translating digital health innovations to improve patient care. This study comprehensively analyzed quantification strategies and data-driven definitions for overdiagnosis reported in the literature. The findings revealed widely diverging results among studies, emphasizing the need for a standard method to quantify the impact of overdiagnosis.