Current Advancements
Medical image analysis has become a cornerstone in disease diagnosis and treatment planning. It allows for non-invasive visualization of internal anatomical structures, enabling clinicians to make accurate decisions (Rohani et al., 2025). Convolutional Neural Networks (CNNs) have demonstrated significant utility in processing Magnetic Resonance (MR) and Computed Tomography (CT) scans, performing tasks such as image segmentation, disease detection, and disease prediction (Kothinti, 2025).
Modern DL systems can interpret imaging data by analyzing features such as tissue size, volume, and shape, while also highlighting critical regions for clinical review. These advancements enable early and accurate detection of conditions like:
- Diabetic retinopathy
- Early-stage Alzheimer’s disease
- Breast nodules in ultrasound scans
- Lung cancer
- Pneumonia
- Brain tumors (Kothinti, 2025)
Notably, Google’s DeepMind has developed DL models capable of diagnosing over 50 eye diseases from retinal scans with high precision (AI Multiple, 2025).
Transformer-based models have significantly improved medical imaging analysis. For instance:
- Breast cancer detection in mammograms has become more accurate.
- UNETR++ enables high-quality 3D medical image segmentation while reducing model parameters and computational costs (“UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation,” 2024).
- Spach Transformer, an encoder-decoder transformer, has been used to enhance PET image denoising performance.
Additionally, the introduction of the Kolmogorov-Arnold Network (KAN) has led to the development of MedKAN—a deep learning framework designed specifically for complex medical image classification tasks (Yang et al., 2025).
Challenges
Despite significant progress, medical image analysis still faces several hurdles. One major issue is the scarcity of large, annotated medical image datasets (Unknown Author, 2024). Creating these datasets often requires labor-intensive manual denoising and labeling, necessitating the involvement of medical professionals. This not only contributes to data scarcity but also introduces class imbalances, which can distort model performance (Unknown Author, 2024).
Medical images are also susceptible to noise and artifacts, which can bias model predictions and make systems vulnerable to adversarial attacks. Furthermore, the “black box” nature of deep learning models fosters distrust among clinicians and medical practitioners (Unknown, 2023).
There remains a critical need for standardized definitions and generalizable approaches to interpretability in medical AI systems (Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2025, 2025). Additionally, algorithmic bias—often stemming from underrepresentation of diverse patient groups in historical data—can lead to inaccurate recommendations and exacerbate existing health disparities (PBS NewsHour, 2025).
Opportunities
Explainable AI (XAI) for Medical Image Diagnosis
There is growing momentum to develop novel explainable AI techniques that provide transparent and clinically interpretable explanations for deep learning predictions. These techniques include:
- Visual attention maps
- Counterfactual explanations
- Concept-based explanations
Robustness to Image Artifacts and Noise
Improving model robustness through enhanced DL architectures or pre-processing methods is vital. This ensures accurate diagnoses even in the presence of common artifacts and noise found in real-world clinical settings.
Federated Learning for Privacy-Preserving Medical Imaging
Federated learning offers a solution to data privacy concerns by enabling collaborative training of medical image analysis models across institutions—without sharing raw patient data. This method also addresses the issue of data scarcity.
Bias Detection and Mitigation in Medical Imaging Datasets
It is crucial to investigate methodologies for identifying and quantifying dataset biases. Concurrently, strategies must be developed to mitigate such biases during model training and evaluation, thereby ensuring equitable performance across diverse populations.
Efficient 3D Medical Image Segmentation
Efforts are underway to create computationally efficient DL models that deliver high-quality 3D segmentation while minimizing parameters and inference time. Innovations like UNETR++ serve as a foundation for this endeavor.
Advanced PET Image Denoising and Reconstruction
Improving PET image quality can enhance diagnostic accuracy while potentially reducing the radiation dose required during scans.
Uncertainty Quantification in Medical Image AI
Developing mechanisms for deep learning models to express uncertainty in predictions is an emerging priority. By providing clinicians with a measure of confidence, such models can support more informed decision-making in critical diagnostic scenarios.