What is the role of Artificial Intelligence (AI) in radiology?

The Role of Artificial Intelligence in Radiology

Artificial Intelligence in radiology currently remains largely at the proof-of-concept stage, with significant potential but requiring substantial improvements in design, development, evaluation, and implementation before achieving widespread clinical adoption.

Current State of AI in Radiology

AI applications in radiology have been developing for decades, evolving from early computer-aided detection (CAD) systems to more sophisticated deep learning approaches. Despite this progression, most AI tools for radiological imaging remain in developmental phases rather than clinical practice 1.

Key Applications

• Image Analysis and Interpretation

  • Detection and classification of abnormalities
  • Identification of regions of interest
  • Triage of images with abnormal findings 1

• Workflow Enhancement

  • Reducing radiologist workload
  • Prioritizing urgent cases
  • Automating routine tasks 1

• Diagnostic Support

  • Assisting non-specialized radiologists in diagnosing rare conditions
  • Reducing interpretive errors associated with increased caseloads 1

Evidence and Limitations

Recent systematic reviews highlight significant gaps between AI development and clinical implementation:

• A 2025 systematic review of AI in soft-tissue and bone tumors found that most studies performed only moderately on the Checklist for AI in Medical Imaging (CLAIM) standards and poorly on FUTURE-AI guidelines 1

• Studies evaluating FDA-approved AI products for breast cancer screening identified important gaps in reporting of data sources, validation approaches, and clinical utility assessment 1

Common Limitations

1. Limited Clinical Validation

   • Most AI tools are developed and tested using retrospective data
   • External validation is often lacking or inadequately reported 1

2. Enriched Datasets

   • Many AI systems are trained on artificially enriched datasets that don't reflect real-world disease prevalence
   • This can lead to poor performance in clinical settings 1

3. Focus on Technical Rather Than Clinical Outcomes

   • Studies primarily report test performance measures (sensitivity, specificity, AUC)
   • Clinically meaningful outcomes like cancer stage at detection or interval cancer rates are rarely assessed 1

Ethical and Implementation Considerations

The ethical deployment of AI in radiology requires careful attention to several principles:

• Transparency and Explainability

  • AI systems should be designed for maximum transparency
  • Clinicians need to understand how AI reaches its conclusions 2

• Accountability

  • Ultimate responsibility remains with human designers and operators
  • Clear frameworks for liability are needed 2

• Data Privacy and Rights

  • AI development must respect patient dignity and privacy
  • Radiology data should not be used for financial gain without benefiting patients 2

Future Directions

For AI to achieve its potential in radiology, several improvements are needed:

1. Better Study Design

   • Define unmet clinical needs
   • Specify intended clinical settings
   • Explain how AI would integrate into clinical workflow 1

2. Improved Development Practices

   • Build on previous work
   • Train with data that reflect real-world usage
   • Enhance explainability of AI systems 1

3. Rigorous Evaluation

   • Ensure biases are evaluated and addressed
   • Compare AI against current best practices
   • Assess clinically meaningful outcomes 1

4. Data Reproducibility and Availability

   • Make documented code and data publicly available
   • Ensure transparency in methods 1

Practical Implications for Radiologists

AI should be viewed as a tool to assist radiologists rather than replace them:

• Complementary Role

  • AI can help identify regions of interest and triage cases
  • Radiologists provide clinical context and final interpretation

• Quality Assurance

  • Radiologists should understand AI limitations
  • Regular auditing of AI performance is essential

• Education and Training

  • Radiologists need training to effectively use AI tools
  • Understanding AI strengths and weaknesses is crucial

Conclusion

While AI shows promise in transforming radiology practice, current evidence indicates that most applications remain at the proof-of-concept stage. Significant improvements in design, development, and evaluation are needed before widespread clinical adoption. Radiologists should approach AI as a complementary tool that may enhance—but not replace—clinical expertise and judgment.