Published Paper Details:

Artificial Intelligence (AI) has emerged as a disruptive and potentially transformative force in modern healthcare, promising significant enhancements in clinical efficacy, operational efficiency, and personalized care delivery. This systematic review investigates the current state of AI implementation across diagnostics, drug discovery, and administrative functions, analyzing both the quantitative impacts and the crucial socio-technical challenges surrounding its adoption. AI systems, particularly deep learning architectures like Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in specific clinical tasks, such as diagnostic imaging analysis, enabling interpretation of chest X-rays for critical conditions in under 10 seconds and reducing Magnetic Resonance Imaging (MRI) scanning times by 30% to 50%. These efficiency gains contribute substantially to optimizing system throughput.[1, 2] Economically, AI-driven remote patient monitoring is projected to save the healthcare industry $200 billion annually by 2028, while predictive systems generate savings of up to 30-45% in high-cost episodes like oncology care.[3, 4] However, successful scaling is hampered by pervasive issues of algorithmic bias, model generalizability (with performance sometimes dropping 20% on external datasets [5]), and the lack of Explainable AI (XAI) in real-world clinical settings. Furthermore, general-purpose generative AI models currently exhibit an overall diagnostic accuracy of only 52.1%, performing significantly worse than expert physicians.[6] The future trajectory of AI integration hinges upon the adoption of privacy-preserving architectures, such as Federated Learning, and advanced personalization techniques, including Digital Twin technology, guided by robust regulatory frameworks like Good Machine Learning Practice (GMLP) .