From Radiology to Pathology: Deep Learning Models in Diagnostic Medicine

Abstract

Deep learning is fueling the latest revolution in diagnostic medicine as radiology, pathology, ophthalmology and dermatology are merged by intelligent pattern recognition. CNNs, transformer models, and multimodal fusion models have achieved near-human if not superhuman accuracies for the task of detection of complex diseases from medical images. In the present study, we systematically reviewed the reported publication of deep learning-based diagnostic system developed from 2017 to 2025, with an aim to evaluate their clinical setting, performance measures and translation challenge. CNN-based diagnostic solutions have transformed radiological lesion detection in computer tomography, magnet resonance imaging and even the mundane X-ray. Meanwhile, reading slides at gigapixel level from digital pathology is now possible thanks to the emergence of self-supervised and attention-based networks. In addition, integrative diagnosis with histopathologic and radiographic data has fueled cross-modality diagnostics to accelerate towards precision medicine in silico. While advances in deep learning have shown great potential to diagnose complex diseases, deep learning models often remain siloed due to lack of complete interoperability across regions and payers, large gaps in interpretability of its clinical assessment and ethical carryover effects associated with biased data or biased trust. Re: Completion)Use of AI methods (e.g., Grad-CAM or SHAP) is increasingly required to generate explanations for single-instance and cohort-level decisions. The regulatory authorities, including US,2025 and EU2024 are emphasizing transparency, reproducibility and ongoing post marketing validation of the AI based medical devices. This is evidence that deep learning won’t automate the diagnosis but change it radically Data-first disciplines, and have algorithms following along perfectly in sync if the clinician has to adapt to assist. Implementing standardized data governance, as well as including adequate interdisciplinary education and training in ethics, is crucial to ensure the accuracy, interpretability and fairness of intelligent diagnostics.