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Research Article Open access CC BY 4.0

Artificial Intelligence in Healthcare: Current Applications, Benefits and Challenges

Anjali Kide-Nandedkar, Shivani Polanwar, Edulaya Nikhitha, Edunoori Shobha Rani, Ettedi Sangeetha, Gajula Abhishek, Gandla Varsha

Journal of Engineering Research and Reports · pp. 258–270 · Published 16 Jan 2026

10.9734/jerr/2026/v28i11774

Abstract

Artificial intelligence (AI) has emerged as a transformative force in healthcare, offering innovative solutions across clinical, diagnostic, and administrative domains. By leveraging advanced computational techniques such as machine learning, deep learning, and natural language processing, AI systems are increasingly integrated into medical imaging, disease diagnosis, personalized treatment planning, virtual healthcare delivery, drug discovery, patient monitoring, and rehabilitation services. These technologies enable the analysis of large and complex datasets with high precision, thereby improving diagnostic accuracy, enhancing clinical decision-making, and supporting evidence-based medical practice. AI-driven tools have demonstrated the potential to outperform conventional methods in specific tasks, particularly in image-based diagnostics and predictive analytics, while simultaneously reducing human error and operational inefficiencies. In addition to clinical applications, AI contributes significantly to healthcare management by automating administrative processes, optimizing resource allocation, and reducing clinician workload. The rapid adoption of AI during the COVID-19 pandemic further underscored its importance in disease surveillance, risk prediction, patient triage, and healthcare system preparedness. Despite these advancements, challenges related to data privacy, ethical considerations, algorithmic bias, interpretability, and regulatory compliance remain critical barriers to widespread implementation. This review was conducted through a structured analysis of recent peer-reviewed literature retrieved from major scientific databases like PubMed, Elsevier, etc. This review provides a comprehensive overview of the current applications of artificial intelligence in healthcare, highlighting recent technological advances, clinical benefits, and implementation challenges. Furthermore, it discusses future perspectives and the role of AI in shaping resilient, efficient, and patient-centered healthcare systems. Overall, artificial intelligence represents a key enabling technology for advancing modern, data-driven healthcare and improving patient outcomes on a global scale.

Artificial intelligence machine learning deep learning healthcare systems clinical decision support medical imaging

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