Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust

TLDR

Deep learning has revolutionized disease detection and enabled robust, accurate computer‑aided diagnostic systems, yet it faces significant threats such as data imbalance, adversarial attacks, and trust and privacy concerns. This paper traverses the major challenges that the deep learning community faces in medical image diagnosis, including data imbalance, adversarial attacks, lack of trust, and ethical and privacy issues. The authors analyze these challenges by reviewing the scarcity of balanced annotated data, the vulnerability of deep neural networks to noisy medical images, and the resulting trust and privacy concerns. The study demonstrates that addressing trust concerns can enable AI autonomy in healthcare.

Abstract

Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage; preoperative planning; intra-operative guidance, etc. However, deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems.