Abstract

Generative Artificial Intelligence (Generative AI) has emerged as a disruptive force in the healthcare business, promising revolutionary breakthroughs in a variety of fields. This paper delves into the underlying concepts of Generative AI and its medical applications. We look at the benefits, such as efficiency and precision, while also discussing the ethical issues of data privacy and prejudice. We demonstrate how Generative AI is altering medical imaging, medication discovery, and personalized patient care through case studies. We talk about implementation tactics and how to overcome obstacles. Finally, we look ahead, forecasting current trends and innovations that will define the future of healthcare. Generative AI has the potential to reshape the medical age by improving diagnosis, treatment, and patient outcomes.

References

• S. Mondal, S. Das, V.G Vrana, How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life, Technologies 11 (2) (2023) 44.
• S. Pal, T. Rabehaja, M. Hitchens, A. Hill, On the design of a flexible delegation model for the Internet of Things using blockchain, IEEE Trans. Ind. Inf. 16 (5) (2019) 3521–3530. M. Jovanovic, M. Campbell, Generative artificial intelligence: trends and prospects, Computer (Long Beach Calif) 55 (10) (2022) 107–112.
• J. Perkins, Immersive metaverse experiences in decentralized 3d virtual clinical spaces: artificial intelligence-driven diagnostic algorithms, wearable internet of medical things sensor devices, and healthcare modeling and simulation tools, Am. J. Med. Res. 9 (2) (2022) 89–104.
• Gill S.S., Kaur R. ChatGPT: vision and challenges. Internet of Things and Cyber-Physical Systems, 2023.
• Q. Cai, H. Wang, Z. Li, X. Liu, A survey on multi-modal data-driven smart healthcare systems: approaches and applications, IEEE Access 7 (2019) 133583–133599.
• Y. Guo, T. Yu, J. Wu, Y. Wang, S. Wan, J. Zheng, Q. Dai, Artificial Intelligence for Metaverse: a Framework, CAAI Artif. Intell. Res. 1 (1) (2022) 54–67.
• P.P Ray, ChatGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope, Internet Things Cyber-Phys. Syst. (2023).
• A. Baía Reis, M Ashmore, From video streaming to virtual reality worlds: an academic, reflective, and creative study on live theatre and performance in the metaverse, Int. J. Performance Arts Digital Media 18 (1) (2022) 7–28.
• A.A Gaafar, Metaverse in architectural heritage documentation & education, Adv. Ecol. and Environ. Res. 6 (10) (2021) 66–86.
• R Godwin-Jones, Emerging spaces for language learning: AI bots, ambient intelligence, and the metaverse, Lang. Learn. Technol. 27 (2) (2023) 6–27.
• W.M. Lim, A. Gunasekara, J.L. Pallant, J.I. Pallant, E. Pechenkina, Generative AI and the future of education: ragnarök or reformation? A paradoxical perspective from management educators, Int. J. Manage. Edu. 21 (2) (2023) 100790.
• M. Poggi, F. Tosi, K. Batsos, P. Mordohai, S. Mattoccia, On the synergies between machine learning and binocular stereo for depth estimation from images: a survey, IEEE Trans. Pattern Anal. Mach. Intell. 44 (9) (2021) 5314–5334.
• W.K. Sleaman, A.A. Hameed, A. Jamil, Monocular vision with deep neural networks for autonomous mobile robots navigation, Optik (Stuttg) 272 (2023) 170162.
• X. Guo, Z. Wang, W. Zhu, G. He, H.B. Deng, C.X. Lv, Z.H Zhang, Research on DSO vision positioning technology based on binocular stereo panoramic vision system, Defence Technol. 18 (4) (2022) 593–603.
• F. Andriulli, P.Y. Chen, D. Erricolo, J.M Jin, Guest editorial machine learning in antenna design, modeling, and measurements, IEEE Trans. Antennas Propag. 70 (7) (2022) 4948–4952.
• X. Wu, F. Guan, A. Xu, Passive ranging based on planar homography in a monocular vision system, J. Info. Process. Syst. 16 (1) (2020) 155–170.
• F. Gao, C. Wang, L. Li, Altitude information acquisition of uav based on monocular vision and mems, J. Intell. Robotic Syst. 98 (2020) 807–818.
• R.M. Samant, M.R. Bachute, S. Gite, K. Kotecha, Framework for deep learning-based language models using multi-task learning in natural language understanding: a systematic literature review and future directions, IEEE Access 10 (2022) 17078–17097.
• D. Ai, G. Jiang, S.K. Lam, C. Li, Computer vision framework for crack detection of civil infrastructure—A review, Eng. Appl. Artif. Intell. 117 (2023) 105478.
• M. Hawkins, Metaverse live shopping analytics: retail data measurement tools, computer vision and deep learning algorithms, and decision intelligence and modeling, J. Self-Governance Manage. Econ. 10 (2) (2022) 22–36.
• R. Watson, The virtual economy of the metaverse: computer vision and deep learning algorithms, customer engagement tools, and behavioral predictive analytics, Linguistic Philos. Investig. (21) (2022) 41–56.
• M. Hawkins, Virtual employee training and skill development, workplace technologies, and deep learning computer vision algorithms in the immersive metaverse environment, Psychosociological Issues Human Resour. Manage. 10 (1) (2022) 106–120.
• S. Gordon, Virtual navigation and geospatial mapping tools, customer data analytics, and computer vision and simulation optimization algorithms in the blockchain-based metaverse, Rev. Contemp. Philos. (21) (2022) 89–104.
• G.H. Popescu, K. Valaskova, J. Horak, Augmented reality shopping experiences, retail business analytics, and machine vision algorithms in the virtual economy of the metaverse, J. Self-Governance Manage. Econ. 10 (2) (2022) 67–81.