Applications of Generative AI in Modern Operating Rooms |
||
|
|
|
© 2024 by IJCTT Journal | ||
Volume-72 Issue-8 |
||
Year of Publication : 2024 | ||
Authors : Suvin Seal | ||
DOI : 10.14445/22312803/IJCTT-V72I8P128 |
How to Cite?
Suvin Seal, "Applications of Generative AI in Modern Operating Rooms," International Journal of Computer Trends and Technology, vol. 72, no. 8, pp. 199-202, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P128
Abstract
The medical industry has long sought to enhance the quality and consistency of patient care, and the advent of Large Language Models (LLMs) presents a promising opportunity to address this challenge. This paper explores the potential application of generative AI, such as GPT-4, within the operating room environment to standardize and improve the quality of patient care. This study synthesizes insights and perspectives from interviews with industry-leading experts, exploring the latest challenges, opportunities, and innovations in healthcare.
Keywords
Large Language Models (LLMs), Surgical workflow optimization, Operating room efficiency, Healthcare democratization.
Reference
[1] Yuan Sun, and Jorge Ortiz, “Rapid Review of Generative AI in Smart Medical Applications,” arXiv preprint, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Hazrat Ali et al., “Revolutionizing Healthcare with Foundation AI Models,” Studies in Health Technology and Informatics, vol. 305, pp. 469-470, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Cheng Peng et al., “A Study of Generative Large Language Model for Medical Research and Healthcare,” NPJ Digital Medicine, vol. 6, no. 210, pp. 1-10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Simona Wójcik et al., “Beyond ChatGPT: What does GPT-4 Add to Healthcare? The Dawn of a New Era,” Cardiology Journal, vol. 30, no. 6, pp. 1018-1025, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Samuel R. Browd, Maya Sharma, and Chetan Sharma, “Generational Frameshifts in Technology: Computer Science and Neurosurgery, The VR Use Case,” arXiv, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Qing Lyu et al., “Translating Radiology Reports into Plain Language Using ChatGPT and GPT-4 with Prompt Learning: Results, Limitations, and Potential,” Visual Computing for Industry, Biomedicine, and Art, vol. 6, no. 9, pp. 1-10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Akshay Ravi, Aaron Neinstein, and Sara G. Murray, “Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors,” American Thoracic Society, vol. 4, no. 3, pp. 282-292, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mohamed Khalifa, Mona Albadawy, and Usman Iqbal, “Advancing Clinical Decision Support: The Role of Artificial Intelligence Across Six Domains,” Computer Methods and Programs in Biomedicine Update, vol. 5, pp. 1-7, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Samuel R. Browd, Christine Park, and Daniel A. Donoho, “Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care,” International Journal of Spine Surgery, vol. 17, no. S1, pp. S26-S33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tatsushi Tokuyasu et al., “Development of an Artificial Intelligence System Using Deep Learning to Indicate Anatomical Landmarks During Laparoscopic Cholecystectomy,” Surgical Endoscopy, vol. 35, no. 4, pp. 1651-1658, 2021.
[CrossRef] [Google Scholar] [Publisher Link]