Unlocking the Potential of Deep Learning in Medical Image Analysis

Deep learning has transformed illness detection and is assisting the healthcare industry in overcoming obstacles related to correctness and reliability to develop effective as well as resilient computer-aided diagnostic tools. Deep learning approaches provide automated AI-driven utilities that need minimum human oversight to execute tasks associated with the medical detection of fractures, malignancies, internal bleeding, preoperative planning, and intra-operative guiding. Nonetheless, deep learning encounters significant challenges within the burgeoning healthcare sector. This article analyzes the primary obstacles facing the deep learning (DL) research and engineering community in the medical image diagnosis, specifically balanced annotated medical image datasets deficiency, adversarial attacks on deep neural networks (DNN) as well as architectures stemming from noisy medical images, patients' & users' lack of trust, along with ethical and privacy concerns of medical data. This research investigates potential for AI autonomy in healthcare by addressing societal trust issues about autonomous intelligent systems.