Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
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Volume 10 Number 1 2026
REVIEWS (Open Access)
Reproducibility, Algorithmic Bias, and Clinical Readiness in Medical Imaging AI: A Lifecycle-Based Synthesis and Multi-Institutional Validation Protocol
Mst Murshida Mahbub 1*, Rabiatul Basria S. M. N. Mydin 2, Chandrarohini Saravanan 2
Integrative Biomedical Research 10 (1) 1-8 https://doi.org/10.25163/biomedical.10110898
Submitted: 17 September 2026 Revised: 04 November 2026 Accepted: 11 November 2026 Published: 13 November 2026
Abstract
Deep learning has transformed diagnostic imaging, pathology, and ophthalmology, with the potential to reduce diagnostic errors and inter-observer variability that still burden routine radiology practice. However, translation of these models from laboratory benchmarks into everyday clinical workflows remains incomplete. This paper reports a structured narrative synthesis of the peer-reviewed and preprint literature addressing three interlocking evidence gaps in medical imaging artificial intelligence (AI): reproducibility failures, the multi-stage propagation of algorithmic bias, and the determinants of clinical readiness. Sources were organized around a five-stage AI lifecycle framework and cross-referenced against reporting frameworks including CLAIM, TRIPOD-AI, and the FUTURE-AI consensus guidelines. Across the cohorts reviewed, internally validated performance metrics (AUROC/DSC frequently exceeding 0.90) declined under external, multi-site testing in every case identified, with reported drops ranging from approximately 3 to 20 percentage points depending on task, modality, and cohort; these figures are drawn from heterogeneous studies using different metrics and are reported here as an illustrative pattern rather than a pooled effect size. Structural mitigation strategies-including Common Data Models, federated learning, and differential privacy-showed promise but remain unevenly adopted. Building on these gaps, we outline a prospective, three-arm multi-institutional protocol addressing reproducibility (RQ1), bias mitigation (RQ2), and clinical readiness (RQ3), intended as a template for future empirical validation rather than a completed study. Bridging the gap between technical proof-of-concept and safe clinical deployment will likely require simultaneous progress on standardized reporting, lifecycle-wide bias auditing, and governance structures capable of monitoring AI performance after initial approval.
Keywords: artificial intelligence, medical imaging, deep learning, reproducibility, algorithmic bias, external validation, clinical readiness, FUTURE-AI guidelines
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