Aug 2026· Natural Resources for Human Health· 0 citations· 20 references
TL;DR
It is concluded that responsible, human-centered AI adoption—anchored in governance, transparency, and equitable design—is essential to realizing intelligent, patient-centered, and sustainable healthcare systems for the future.
Abstract
Artificial Intelligence (AI) is fundamentally reshaping healthcare delivery by enabling systems that are predictive, preventive, and personalized. This paper synthesizes recent advances in machine learning, deep learning, natural language processing, and generative AI, and examines how these technologies are applied across disease forecasting, clinical decision support, medical imaging, remote patient monitoring, and precision medicine. Drawing on published literature and industry evidence, the paper presents a conceptual framework linking multi-modal health data, AI analytics, and three interdependent pillars of next-generation care, culminating in improved patient outcomes and sustainable health-system performance. The discussion further evaluates real-world deployments, summarizes representative performance findings reported in the literature, and critically examines persistent barriers including algorithmic bias, data privacy, explainability, and regulatory compliance. The paper concludes that responsible, human-centered AI adoption—anchored in governance, transparency, and equitable design—is essential to realizing intelligent, patient-centered, and sustainable healthcare systems for the future.
The research explores the transformative impact of Artificial Intelligence (AI) on healthcare, focusing on its advancements in diagnostics and disease prediction. It traces the evolution of machine learning algorithms from rule‑based systems to sophisticated deep learning techniques, such as CNN for medical imaging and...
Hanan R. Alnjar· International journal of re...· 0 citations
It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begu...
V. Ramawat, G.PRABHAKARAN, Pinki Das et al.· International journal of com...· 0 citations
Artificial intelligence (AI), particularly machine learning (ML), is reshaping life sciences by enabling the analysis of complex biological and clinical data for disease prediction, biomarker discovery, and personalized healthcare. However, existing research remains fragmented across individual applications, while conc...
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal...
Nasheeda T. M.· International Journal of Tec...· 0 citations
Generative AI (GenAI) and Large Language Models (LLMs) have rapidly evolved and showcased transformational capabilities in precision medicine and predictive healthcare. By harnessing enormous volumes of healthcare data, these technologies can add value to the clinical decision-making process and help in treatment optim...
Phani Raj Kumar Bollipalli· International Journal of Hum...· 0 citations
To examine how artificial intelligence (AI) and machine learning (ML) can advance pharmacogenomics and personalized medicine by improving drug-response prediction, treatment selection, dosing, and clinical decision support, while identifying the major barriers to clinical implementation. This narrative review criticall...
Arwa Al Anber· Natural Resources for Human...· 0 citations
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