Preventing Late Diagnosis Through AIML and Computer Vision: A Strategic Approach to Early Disease Detection in Medical Imaging

Delayed diagnosis of life-threatening conditions such as cancer, stroke, and pneumonia remain a persistent global healthcare challenge. Human-dependent interpretation of medical imaging is constrained by subjectivity, time consumption, and a global shortage of radiologists. In contrast, the integration of Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV) offers a transformative pathway to early detection. This paper presents a comprehensive AI/ML-CV framework, showcasing its capabilities in automating the detection of anomalies in radiological data (e.g., X-rays, CT scans, MRIs), and improving diagnostic speed and precision. We discuss real-world applications, model performance benchmarks, explainability, ethical concerns, and a strategic implementation roadmap for healthcare institutions.