What Medical Professions Will be Affected by Artificial Intelligence Soon?
Keywords:
artificial Intelligence, machine learning, medical professionsAbstract
The integration of artificial intelligence (AI) into healthcare is ushering in a transformative era for medical professions. The article explores AI’s current state and future implications across various medical specialties, including anesthesiology, surgery, radiology, pathology, cardiology, oncology, primary care, pharmacy, and nursing. AI technologies like machine learning and deep learning are revolutionizing diagnostic accuracy, personalized treatment plans, and healthcare workflows, ultimately leading to improved patient outcomes. This transformation necessitates healthcare professionals’ adaptation and collaboration with AI systems, highlighting the significance of education, ethical practice, and patient trust. Ethical considerations, including fairness, transparency, and patient-centered care, play a pivotal role in preserving trust in AI-enabled healthcare. This article offers a comprehensive and current examination of AI’s profound impact on medical professions. It provides valuable insights into responsibly navigating this transformative journey, ensuring the continued delivery of high-quality patient care in an AI-augmented healthcare landscape.
References
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial intelligence in healthcare, Elsevier. 2020;p25-60.
Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. J Anesth. 2020;132:379-94.
Bellini V, Carna ER, Russo M, et al. Artificial intelligence and anesthesia: a narrative review. Ann Transl Med. 2022;10:1-14.
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689.
Ahmed U, Iqbal K, Aoun M. Natural language processing for clinical decision support systems: a review of recent advances in healthcare. J Intell Connect Emerg Technol. 2023;8:1-16
Brady AP, Bello JA, Derchi LE, et al. Radiology in the era of value-based healthcare. a multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA. Radiology. 2021;298:486-91.
Saini AS. AI revolution in radiology: how Philips transforms clinical cases for better healthcare. 2023 [cited 2023 Sep 30]. Available from: https://www.linkedin.com/pulse/ai-revolution-radiology-how-philips-transforms-clinical-aman-s-saini
Park H, Kim KE, Choi Y-J, et al. Analysis of Watson for oncology and clinicians’ treatment recommendations for patients with breast cancer in Korea: a single center experience. Indian J Cancer. 2023;60:211-6.
Wbcomdesigns.com. 10 top artificial intelligence (AI) applications in healthcare; 2023 [updated 2023 July 5; cited 2023 Oct 1]. Available from: https://wbcomdesigns.com/top-artificial-intelligence-applications-in-healthcare/#2_Google_DeepMind_Health
Patel SB, Lam K. ChatGPT: the future of discharge summaries? Lancet Digit Health. 2023;5:e107-8.
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689.
Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. J Anesth. 2020;132:379-94.
Chu LF, Erlendson MJ, Sun JS, Clemenson AM, Martin P, Eng RL. Information technology and its role in anaesthesia training and continuing medical education. Best Pract Res Clin Anaesthesiol. 2012;26:33-53.
Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring artificial intelligence in anesthesia: a primer on ethics, and clinical applications. Surgeries. 2023;4:264-74.
Pakkasjärvi N, Luthra T, Anand S. Artificial intelligence in surgical learning. Surgeries. 2023;4:86-97.
Najjar R. Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics. 2023;13:2760.
Lee LIT, Kanthasamy S, Ayyalaraju RS, Ganatra R. The current state of artificial intelligence in medical imaging and nuclear medicine. BJR Open. 2019;1:20190037.
Moxley-Wyles B, Colling R, Verrill C. Artificial intelligence in pathology: an overview. Diagn Histopathol. 2020;26:513-20.
Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: hope for the future and power for the present. Front Cardiovasc. 2022;9:945726.
Adir O, Poley M, Chen G, et al. Integrating artificial intelligence and nanotechnology for precision cancer medicine. Adv Mater. 2020;32:1901989.
Papachristou N, Kotronoulas G, Dikaios N, et al. Digital transformation of cancer care in the era of big data, artificial intelligence and data-driven interventions: navigating the field. Semin Oncol Nurs. 2023;39:151433.
Zhang P, Boulos MNK. Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet. 2023;15:286.
Scardoni A, Balzarini F, Signorelli C, Cabitza F, Odone A. Artificial intelligence-based tools to control healthcare associated infections: a systematic review of the literature. J Infect Public Health. 2020;13:1061-77.
Raza MA, Aziz S, Noreen M, et al. Artificial intelligence (AI) in pharmacy: an overview of innovations. Innov Pharm. 2022;13:10.24926/iip.v13i2.4839.
Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of artificial intelligence in nursing care: a systematic review. J Nurs Manage. 2023;2023:Article ID3219127.
Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020;22:e17620.
Dwivedi YK, Hughes L, Ismagilova E, et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inform Manage. 2021;57:101994.
Prakash S, Balaji JN, Joshi A, Surapaneni KM. Ethical conundrums in the application of artificial intelligence (AI) in healthcare-a scoping review of reviews. J Pers Med. 2022;12:1914.
Vesterby MS, Pedersen PU, Laursen M, et al. Telemedicine support shortens length of stay after fast-track hip replacement. Acta Orthop. 2017;88:41-7.
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