Next-Generation AI for Breast and Prostate Cancer Diagnosis in Bangladesh
An Implementation Framework and Validation Roadmap
DOI:
https://doi.org/10.18034/abcjar.v15i1.805Keywords:
Artificial intelligence, Breast cancer diagnosis, Prostate cancer diagnosis, Bangladesh, Federated learning, Clinical validationAbstract
Breast cancer is the most frequently diagnosed cancer among women in Bangladesh, while prostate cancer is an increasing health concern among older men, contributing to a growing national cancer burden. Although artificial intelligence (AI) has demonstrated promising performance in breast imaging and prostate magnetic resonance imaging, most existing models have been developed and validated in high-income settings, limiting their direct applicability to Bangladesh because of differences in disease patterns, imaging quality, clinical workflows, infrastructure, and resource availability. This article proposes a context-specific three-tier AI implementation framework designed to support safe and effective breast and prostate cancer diagnosis in Bangladesh. The framework was developed through a synthesis of AI implementation literature, Bangladesh-specific healthcare challenges, internationally recognized reporting and evaluation standards (STARD-AI, CLAIM, CONSORT-AI, DECIDE-AI, and FUTURE-AI), and WHO guidance on ethical AI. It consists of edge-based AI triage for district hospitals, multimodal clinical decision support for tertiary care centers, and a federated learning infrastructure that enables continuous model improvement while preserving institutional data privacy. A staged validation roadmap, including retrospective validation, prospective silent evaluation, clinical implementation, pragmatic trials, and post-deployment monitoring, is proposed to ensure safety, transparency, and clinical reliability. Rather than replacing clinicians, the framework positions AI as an assistive technology that could strengthen early cancer detection, optimize specialist resources, and support scalable implementation across Bangladesh and similar low- and middle-income countries following rigorous local validation.
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