Data to Diagnosis: A Systematic Review of AI/ML in Healthcare - Sheetal Singh; Khushi Singh; Sujeet Kumar; Aritro Chakraborty; Harshal Singh (International Journal of Biomedical Innovations and Engineering, 2025)
Artificial Intelligence (AI) and Machine Learning (ML) are fast revolutionizing the diagnosis of healthcare by augmenting accuracy, speed, and efficiency. AI/ML technologies facilitate earlier and more accurate disease identification with advanced algorithms for image processing, predictive modelling, and pattern recognition, frequently outperforming conventional diagnostic techniques. This review delves into the key contribution of AI/ML in contemporary healthcare, such as its use in clinical data analysis, imaging reports, and patient histories to enable more timely and informed diagnoses. Emphasis is given to real-time diagnostic functions based on AI/ML, whose potential to streamline processes, eliminate diagnostic errors, and maximize resource allocation is stressed. Data security, ethical considerations, algorithm openness, and regulatory compliance are some of the obstacles that must be properly addressed despite their revolutionary potential. This paper gives an extensive overview of contemporary AI/ML deployments in medical diagnostics, advantages they bring, and challenges that need to be overcome to realize safe, efficient, and large-scale implementation.
Keywords: Artificial Intelligence (AI); Clinical data analysis; Diagnostic Accuracy; Machine learning (ML); Medical imaging; Pattern recognition; Predictive modeling; early disease detection; healthcare diagnostics; image processing; real-time diagnosis