AI in Daily Radiology Practice: A Radiologist's Perspective on Commercial Solutions

Introduction

As a practicing radiologist, I've witnessed firsthand the rapid integration of artificial intelligence into our daily workflows some of them isolated from hospital vertical. Its better the AI systems developed in-house by major research institutions, some under the umbrella of the big 5 companies my experience , its better than revolves around commercially available AI products that promise to enhance diagnostic accuracy and efficiency in routine clinical practice.

Practical Applications in a Workflow

Chest Imaging: My Most Frequent AI Encounter

In chest radiology, AI has become a reliable second reader. The algorithms excel at detecting pneumothorax, lung nodules, and consolidations—conditions that demand both speed and accuracy in emergency settings. I've noticed that AI assistance particularly shines during overnight shifts when fatigue might affect my performance the problem is the integration to RIS-PACS. that were not desinged to AI integration. The AI doesn't suffer from the cognitive biases that sometimes influence interpretations after seeing multiple similar cases. Denerative AI solution like ARIES from DELL is a winner one.

Neuroimaging: Complex but Rewarding

Brain imaging presents more challenges. While AI performs admirably in detecting large vessel occlusions in stroke patients—critical for time-sensitive interventions—I remain cautious about its limitations in subtle pathologies. The AI systems I use demonstrate high consistency, processing large volumes of data without the variability that comes with human factors like fatigue or workload pressure. Here Monai is plenty of algorritms for brain hemorrage, and brain mapping.

The Reality Check: Limitations I've Experienced

Despite the promise, I've learned to approach AI with measured expectations. In my experience with medical image registration tasks, traditional numerical optimization methods still outperform deep learning approaches. I've seen cases where AI-generated reconstructed images contained artifacts that could potentially mislead diagnosis—a sobering reminder that these tools require careful validation.(in CRX with superposition of diseases e.g.)

The "black box" nature of many AI systems troubles me. When an algorithm flags an abnormality, I often cannot understand the reasoning behind its decision. This lack of interpretability makes me rely heavily on my clinical judgment rather than blindly accepting AI recommendations.

Integration Challenges

Implementing AI in daily practice isn't as seamless as vendors promise. The regulatory landscape is complex—most product fall under Class IIa or Class I certifications, indicating moderate-risk classifications. However, interoperability remains a significant hurdle. Some hospital information systems don't always communicate effectively with AI platforms, requiring manual workarounds that sometimes negate the efficiency gains.

Performance Comparison: Human vs. Machine

In my experience, AI excels in speed and consistency but lacks the adaptability I bring to complex cases. While AI can process hundreds of images rapidly with uniform accuracy, I have handled approximately 50-100 studies per session with deeper contextual understanding. The AI doesn't fatigue, but it also doesn't recognize when a case falls outside its training parameters. Overfitting is a reality!

I've observed that AI performs best in high-volume, pattern-recognition tasks—exactly what dominates emergency radiology. However, for nuanced cases requiring clinical correlation or unusual presentations, my medical training and experience remain irreplaceable. Its better to train dice score models over 0.7

Practical Implementation Insights

Data Quality Matters

One lesson I've learned is that AI performance heavily depends on data quality. Images with motion artifacts, poor contrast, or technical inadequacies often confuse the algorithms. This has made me more conscious of imaging protocols and the importance of optimal image acquisition.The first step is dive depeer in onsite radiology studies archive.

The Learning Curve

Adapting to AI-assisted reporting required adjusting my workflow. Initially, I found myself second-guessing both the AI and myself. Over time, I've developed a collaborative approach: I use AI as a screening tool for obvious pathologies while maintaining vigilance for cases where human insight is crucial. The use of AI at the begining is only text base reports.

Looking Forward

The AI market appears to be stabilizing after rapid growth through 2020. This maturation brings both opportunities and challenges. While we're seeing fewer revolutionary new products, existing solutions are becoming more refined and clinically validated. You can attend to RSNA to build carpograms AI database, anonymous studies using AI, working on studies by local AI models like in Jan.ai

Conclusion

AI in radiology isn't about replacement—it's about augmentation. In my daily practice, these tools serve as intelligent assistants that help me work more efficiently while maintaining diagnostic quality. The key is understanding both the capabilities and limitations of these systems. Radiologist need to undertand how to use AI. For instance have account in Github, Collab, etc.

As we move forward, I believe the most successful AI implementations will be those that enhance rather than replace clinical judgment. The future lies not in choosing between human intelligence and artificial intelligence, but in leveraging both to provide the best possible patient care.

The revolution is here, but it's more evolution than replacement. As radiologists, we must embrace these tools while maintaining the critical thinking and clinical insight that define our profession.