How do we move from black box AI to clinically trustworthy decision support?
A newly published study in Telehealth and Medicine Today (THMT) explores an explainable deep transfer learning framework for chest X-ray–based pulmonary disease diagnosis—designed to support
clinicians, not replace judgment.
What this paper tackles
- The challenge of diagnostic variability in chest X-ray interpretation
- The use of transfer learning and ensemble architectures (including ConvNeXt, EfficientNetV2, and Swin Transformer) to improve model
generalizability
- The importance of explainability in AI-driven clinical decision support
- Practical approaches to deploying high-performance models without prohibitive computational cost
Rather than focusing on raw performance metrics, the study emphasizes methodological rigor,
transparency, and clinical relevance—key requirements for real-world adoption of AI.
What makes this citable?
- Addresses a high-impact, global clinical problem (pulmonary disease detection)
- Applies and compares state-of-the-art deep learning architectures within a
reproducible framework
- Advances the conversation on explainable AI (XAI) in healthcare—an area of growing regulatory and clinical importance
- Provides a strong methodological reference for researchers working in medical imaging, AI validation, and clinical decision support systems
Curious
about the findings and implications?
The authors invite readers to explore the full paper and assess the approach in detail.
Read the article at DOI https://doi.org/10.30953/thmt.v10.594
Authors:
R. Sriramkumar, M.Tech; K. Selvakumar, PhD; J. Jegan, PhD
This is citable, peer-reviewed research contributing to the evidence base for explainable AI in diagnostic imaging.
