Transforming medical diagnosis: a comprehensive review of AI and ML technologies

Ankita Gaonkar; Shree Rath; Rahini S; Manav Jain; Raman Swathy Vaman; Anindita Das; Samiya Manal; Divjot Singh Lamba; Purnoor Kaur; SUKRITI YADAV; Sanjev Dave; Ashlesha Ashok Tawde; Swati Misra; Sowmisri BM; Chanchal Goyal; Shreekant Bharti; Krishna Chaitanya Amerneni

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in healthcare, particularly in medical diagnostics. This review explores the role of AI and ML in revolutionizing disease detection, treatment planning, and personalized healthcare. AI technologies, including deep learning (DL), natural language processing (NLP), and advanced data analytics, enable the rapid and accurate processing of complex medical datasets such as imaging, genetic information, and electronic health records (EHRs). By identifying subtle patterns and abnormalities, AI systems enhance diagnostic accuracy in areas such as oncology, cardiology, neurology, dermatology, and infectious diseases. The integration of AI in cancer diagnosis, for instance, has improved early detection through advanced imaging analysis using convolutional neural networks (CNNs) and deep learning models. Similarly, in cardiovascular diseases, AI enhances electrocardiogram (ECG) analysis and risk stratification, enabling early intervention. Neurological disorders such as Alzheimer's and Parkinson's benefit from AI tools that analyze neuroimaging, speech, and motor patterns for early diagnosis and progression monitoring. In diabetes management, ML models predict disease onset, personalize treatment plans, and improve blood glucose monitoring. Dermatological and ophthalmological applications leverage AI-driven image recognition tools to diagnose skin lesions, diabetic retinopathy, and glaucoma with high precision. Despite its potential, the adoption of AI in healthcare faces challenges, including data privacy concerns, algorithmic bias, and regulatory hurdles. Addressing these issues through robust validation, transparency, and ethical frameworks is essential for wider implementation. This review highlights the future prospects of AI in healthcare, such as precision medicine, wearable technology, and AI-driven telemedicine, emphasizing its potential to enhance efficiency, reduce costs, and improve patient outcomes. As AI technologies continue to evolve, they promise a more accurate, accessible, and personalized approach to medical diagnosis and treatment.

Keywords:

Breastfeeding practices, Afghanistan, Sociodemographic factors, Socioeconomic factors

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Gaonkar , A., Rath, S., S, R., Jain, M., Swathy Vaman, R., Das , A., … Chaitanya Amerneni, K. (2025). Transforming medical diagnosis: a comprehensive review of AI and ML technologies. Evidence Public Health, 1(1). Retrieved from http://eph.evidencejournals.com/index.php/j/article/view/10

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Copyright (c) 2025 Ankita Gaonkar , Shree Rath, Rahini S, Manav Jain, Raman Swathy Vaman, Anindita Das , Samiya Manal, Divjot Singh Lamba, Purnoor Kaur , SUKRITI YADAV , Sanjev Dave , Ashlesha Ashok Tawde, Swati Misra, Sowmisri BM , Chanchal Goyal , Shreekant Bharti , Krishna Chaitanya Amerneni

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