Artificial Intelligence in Healthcare: A Comprehensive Review of Applications, Challenges, and Future Directions

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare by improving diagnostic accuracy, optimizing workflows, and accelerating research. In This review key aspects of AI applications in diagnostic imaging, predictive modeling, clinical decision support, robotic surgery, and drug discovery. For example, convolutional neural networks (CNNs) have achieved over 89% accuracy in interpreting chest radiographs, while generative models like GENTRL have identified novel drug compounds in under two months. AI tools now rival or surpass human experts in early sepsis detection, skin cancer classification, and stroke risk prediction using electronic health records (EHRs). Natural language processing (NLP) also enables the extraction of actionable insights from unstructured clinical texts, aiding personalized care. Despite these advancements, ethical and practical concerns persist. Issues such as algorithmic bias, lack of transparency, and data privacy risks challenge the safe integration of AI in clinical practice. Models trained on biased datasets may worsen health disparities, and the opaque nature of many AI systems limits clinician trust, underscoring the need for explainable AI (XAI). This review synthesizes current literature to assess AI’s strengths, limitations, and future potential in healthcare. It calls for robust validation, interdisciplinary collaboration, inclusive data practices, and the creation of ethical frameworks to guide AI deployment. When responsibly implemented, AI has the potential to enhance clinical decision-making, reduce diagnostic errors, and improve health outcomes globally.

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