GENERATIVE AI IN HEALTHCARE: REVOLUTIONIZING DISEASE DIAGNOSIS, EXPANDING TREATMENT OPTIONS, AND ENHANCING PATIENT CARE

Authors

  • Nasrullah Abbasi Washington University of Science and Technology, Alexandria, Virginia, USA Author
  • Nizamullah FNU Washington University of Science and Technology, Alexandria, Virginia, USA. Author
  • Shah Zeb Washington University of Science and Technology, Alexandria, Virginia, USA. Author
  • MD Fardous Washington University of Science and Technology, Alexandria, Virginia, USA. Author

DOI:

https://doi.org/10.60087/jklst.vol3.n3.p127-138

Keywords:

Generative AI in Healthcar , healthcare , disease diagnosis , patient care

Abstract

Generative AI, an advanced subset of artificial intelligence, has emerged as a transformative force in healthcare, offering unprecedented capabilities in disease diagnosis, treatment development, and patient care. This article explores the integration of generative AI technologies, such as Generative Adversarial Networks (GANs) and transformers, in medical applications. We discuss how these technologies are enhancing diagnostic accuracy, personalizing treatment plans, and improving patient outcomes. Through a comprehensive review of current literature and case studies, we highlight the potential and challenges of implementing generative AI in clinical settings. This research underscores the need for continued innovation and ethical considerations to fully realize the benefits of AI-driven healthcare.

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Published

15-08-2024

Issue

Vol. 3 No. 3 (2024): Exploring Ethical Dilemmas in Artificial Intelligence and Machine Learning

Section

Articles

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Copyright (c) 2024 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

©2024 All rights reserved by the respective authors and JKLST.

How to Cite

Abbasi, N., FNU, N. ., Shah Zeb, & MD Fardous. (2024). GENERATIVE AI IN HEALTHCARE: REVOLUTIONIZING DISEASE DIAGNOSIS, EXPANDING TREATMENT OPTIONS, AND ENHANCING PATIENT CARE. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 127-138. https://doi.org/10.60087/jklst.vol3.n3.p127-138