. Introduction to the Topic

Artificial intelligence (AI) is rapidly transforming healthcare, particularly in diagnostics. Radiology, pathology, and cardiology are seeing AI tools outperform or complement traditional human-based methods. A meta-analysis can provide robust evidence by combining results from multiple studies to assess whether AI truly improves diagnostic accuracy and patient outcomes.

2. Why This Topic Matters

The debate between AI tools and conventional diagnostic methods is not just academic—it has direct clinical, ethical, and financial implications. Understanding the comparative effectiveness helps guide healthcare investments, policymaking, and physician training.

3. Methodological Considerations in Meta-Analysis

When conducting a meta-analysis on this subject, researchers must carefully select inclusion criteria. For example:

• AI-based tools (deep learning, machine learning algorithms).

• Traditional methods (radiologist interpretation, manual ECG reading, etc.).

• Outcomes of interest (sensitivity, specificity, accuracy, patient mortality, cost-effectiveness).
  Statistical models like random-effects may be used due to study heterogeneity.

4. Challenges in the Literature

• Publication bias: Studies showing AI superiority may be more likely to be published.

• Variability: Different AI models and healthcare settings complicate comparisons.

• Ethical Concerns: Replacing clinicians with AI raises issues about responsibility and trust.

5. Future Directions

The meta-analysis can highlight gaps in current evidence, such as the need for long-term patient outcome studies or standardized reporting of AI models. This can guide future clinical trials and policy development.

6. Examples

• A study comparing AI-based mammography interpretation vs. radiologists found AI improved early cancer detection rates by 8%.

• In cardiology, AI-driven ECG analysis showed higher sensitivity in detecting atrial fibrillation compared to manual reading.

• A meta-analysis of AI in pathology suggested AI systems achieved diagnostic accuracy levels comparable to expert pathologists.