Application of Artificial Intelligence in Medical Sciences, Healthcare, and Treatment: A Human-Centered Perspective

Document Type : Perspective

Authors

1 Department of Internal Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Veterinary Medicine, Islamic Azad University, Karaj Branch, Iran

3 Department of Radiology, Faculty of Medicine, AJA University of Medical Science, Tehran, Iran

4 Department of Anatomical Sciences, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran

Abstract

Artificial Intelligence (AI) is a discipline within computer science that utilizes large datasets to simulate human intelligence, thereby supporting healthcare professionals and students in achieving more effective training and treatment outcomes. This article examines the application of AI in the medical sciences, healthcare, and treatment across six sections, investigating both the achievements and limitations of AI. The growing integration of AI into medical sciences has led to notable progress in medical education, patient care, and clinical practice. Increasing students' practical skills, easier access to large amounts of data, faster diagnosis, personalized treatment, and smarter care are among the benefits of using AI, which justifies the increasing use of this technology. Today, the benefits of AI in medical education are widely acknowledged. One of the key advantages is making the learning process more understandable for learners from an academic perspective and being recognized as a major transformation in medical education. The use of AI has always been accompanied by challenges. However, despite these challenges, AI has a promising future in education and healthcare, provided that its implementation is guided by human-centered design, ethical oversight, and appropriate governance structures. This Perspective emphasizes the interconnected nature of AI use across medical education, clinical practice, and healthcare systems, and argues that AI should be positioned as a supportive rather than autonomous force in medicine. Its progress depends on stronger interdisciplinary collaboration between computer scientists and medical researchers to identify and address key challenges and to implement effective data-driven approaches to achieve this aim.

Highlights

Hamid Reza Karbalaei-Musa (Google Scholar)

Meysam Hassani Moghaddam (Google Scholar)

Keywords

References

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Interdisciplinary Journal of Virtual Learning in Medical Sciences
Volume 16, Issue 4 - Serial Number 63
December 2025
Pages 427-433

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History

  • Receive Date: 05 March 2025
  • Revise Date: 16 November 2025
  • Accept Date: 26 November 2025
  • Publish Date: 01 December 2025

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