The AI Revolution in Medicine: A Comprehensive Review of Diagnostic Breakthroughs, Operational Challenges, and the Roadmap to Integrated Healthcare
Daksh Chetan
Scholar, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Dhruv Pathak
Scholar, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Shubham Nath Tiwari
Scholar, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Farheen Siddiqui
Assistant Professor, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
📌 DOI: https://doi.org/10.63920/tjths.52021
🔑 Keywords: Artificial Intelligence, Business Transformation, Machine Learning, Automation, Digital Transformation, Predictive Analytics
📅 Publication Date: 17 April 2026
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This work is licensed under a Creative Commons Attribution 4.0 International License
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Abstract:
Artificial Intelligence (AI) has emerged as a disruptive paradigm in computer science, offering the potential to fundamentally transform clinical practice and the global delivery of healthcare through high-fidelity diagnostic automation. This review evaluates the recent breakthroughs in deep learning architectures that have achieved parity with human diagnostic accuracy in radiology, pathology, dermatology, and ophthalmology. We analyze the technical integration of predictive modeling in oncology and neurology, demonstrating how algorithmic precision facilitates optimized pharmacological selection and disease progression mapping. Furthermore, the study examines the operational efficiencies gained through robotic-assisted Surgical systems and the automation of Electronic Health Records (EHR), identifying significant reductions in clinician cognitive load. Despite these advancements, we identify critical "operational friction" in the form of data privacy vulnerabilities, algorithmic bias, and a lack of model interpretability. We propose a strategic roadmap for the development of resilient, safe AI systems, emphasizing federated learning and decentralized health monitoring as essential components of the next-generation healthcare architecture. This paper concludes that the transition to an integrated healthcare ecosystem depends on the establishment of rigorous ethical frameworks and national regulatory standards to ensure equitable and reliable medical outcomes
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📖 How to Cite
Daksh C., Dhruv P., Shubham Nath T., Ms. Farheen S. (2026). The AI Revolution in Medicine: A Comprehensive Review of Diagnostic Breakthroughs, Operational Challenges, and the Roadmap to Integrated Healthcare. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52021
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