Published Paper Details:

The intersection of artificial intelligence (AI) and human intuition in clinical diagnostics has sparked a paradigm shift in medical decision-making. With advances in machine learning, deep learning, and natural language processing, AI systems have demonstrated impressive capabilities in pattern recognition, differential diagnosis, and disease prediction. However, the irreplaceable cognitive heuristics, experiential learning, and emotional intelligence of human clinicians remain vital in ambiguous and complex scenarios. This research paper presents a comprehensive comparative analysis of AI-based diagnostic tools versus human medical professionals across a range of diseases, including radiology, pathology, cardiology, and oncology. Using real-world datasets, clinical case simulations, and literature synthesis, this study evaluates accuracy, sensitivity, specificity, diagnostic time, error rates, and contextual understanding. The results indicate that while AI outperforms in speed and large-scale data analysis, human intuition excels in handling rare presentations, ethical judgments, and patient-specific nuances. A hybrid model leveraging both AI precision and human intuition emerges as the optimal solution for future healthcare systems. This study concludes that AI is not a replacement but a robust augmentation to human expertise, reshaping the future of medical diagnosis through collaborative intelligence.