Artificial Intelligence in Healthcare: Enhancing Efficiency, Ensuring Equity, and Restoring Empathy - Sevval, Kandeniz; Yusuke, Nakamura (Journal of Big Data Research, 2025)
Artificial Intelligence (AI) is emerging as a transformative force across many sectors, with healthcare representing both one of the most promising and most challenging areas of application. This review summarizes current and future applications of AI in healthcare, focusing on its potential to improve diagnosis, therapy, chronic disease management, and overall patient care, while also alleviating physicians’ workload. Recent literature demonstrates that AI systems can reduce diagnostic errors/delays by mitigating cognitive biases, support imaging and pathology through improved accuracy and speed, and prevent prescribing errors by integrating pharmacogenomic and clinical data into decision-support systems. In chronic disease management, AI-powered wearable devices enable continuous monitoring and early detection of conditions such as atrial fibrillation, thereby reducing the risk of stroke and long-term disability, particularly in elderly people. Therapeutic applications include AI-driven drug discovery, personalized oncology, and tailored medicine that integrates multi-omics and lifestyle data. Beyond direct medical intervention, AI contributes by automating routine tasks, optimizing workflows, and facilitating greater patient–clinician interaction. Despite these benefits, significant challenges remain, including issues of data quality, privacy, security, equity, and the need for transparency and trust in “black box” systems. Looking ahead, the integration of multimodal data, digital twins, and robotics is expected to advance more comprehensive, equitable, and human-centered care. We conclude that, when applied ethically and responsibly, AI should not replace clinicians but rather serve as a powerful partner that enhances medicine by restoring empathy and humanity.
Keywords: Artificial Intelligence; Healthcare; Diagnosis; Remote Monitoring; Personalized Medicine; Workload Reduction; Patient-Centered; Ethics