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

Author(s)	Amit Jha
Country	United States
Abstract	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.
Keywords	Medical Imaging AI, Computer Vision in Healthcare, Early Diagnosis, Deep Learning, CNNs, Radiology Automation, Diagnostic Accuracy, Healthcare AI Governance, Explainable AI, Predictive Analytics.
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.54997

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