Download pdf
Artificial intelligence in healthcare: Opportunities and challenges
- 1 University College London
* Author to whom correspondence should be addressed.
Abstract
The development of Artificial Intelligence (AI) in healthcare has had a significant impact on healthcare. AI in healthcare can provide more accurate diagnoses and interventions for patients. AI can predict, diagnose, and treat diseases, facilitate the maximum use of healthcare resources by integrating medical information, increase efficiency, and reduce overcrowding of healthcare resources. However, the application of AI in healthcare also faces challenges such as accountability, algorithmic security, and data privacy. This paper discusses the application of AI in healthcare and explores the challenges faced by AI, in-cluding accountability traceability, algorithmic safety, data security, and ethical issues, and makes targeted recommendations. This study provides an in-depth exploration of the application of AI in healthcare, helping to improve the accuracy and efficiency of AI ap-plications in healthcare, as well as providing necessary guidance and references for opti-mizing and enhancing AI technologies.
Keywords
AI, healthcare, opportunities, and challenges
[1]. Topol, E. J.: High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56 (2019).
[2]. Eurostat.: Ageing Europe—Statistics on Population Developments. Eurostat, (2020).
[3]. WHO Homepage, https://www.who.int/news-room/fact-sheets/detail/ageing-and-health, last ac-cessed 2022/10/01.
[4]. WHO Homepage, https://www.who.int/health-topics/health-workforce#tab=tab_1, last accessed 2019/08/07.
[5]. WHO Homepage, https://www.euro.who.int/en/health-topics/noncommunicable-diseases/mentalhealth/news/news/2012/10/depression-in-europe/depression-in-europe-facts-and-figures, last accessed 2012.
[6]. UNESCO.: Artificial Intelligence and Gender Equality: Key Findings of UNESCO’S Global Dialogue, (2020).
[7]. Europarl Homepage, https://www.europarl.europa.eu/stoa/en/document/EPRS_STU(2022) 729512, last accessed 2022/01/06.
[8]. Quer, G., Arnaout, R., Henne, M., Arnaout, R.: Machine learning and the future of cardiovascu-lar care: JACC state-of-the-art review. Journal of the American College of Cardiology 77(3), 300-313 (2021).
[9]. Jamthikar, A. D., Gupta, D., Saba, L., Khanna, N. N., Viskovic, K., Mavrogeni, S., Suri, J. S.: Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound. Computers in Biology and Medicine 126, 104043 (2020).
[10]. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., Madabhushi, A.: Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology 16(11), 703-715 (2019).
[11]. Bejnordi, B. E., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., CAMELYON16 Consortium.: Diagnostic assessment of deep learning algorithms for detec-tion of lymph node metastases in women with breast cancer. Jama 318(22), 2199-2210 (2017).
[12]. Miotto, R., Li, L., Kidd, B. A., Dudley, J. T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports 6(1), 1-10 (2016).
[13]. Berlyand, Y., Raja, A. S., Dorner, S. C., Prabhakar, A. M., Sonis, J. D., Gottumukkala, R. V., Yun, B. J.: How artificial intelligence could transform emergency department operations: the American journal of emergency medicine 36(8), 1515-1517 (2018).
[14]. Menke, N. B., Caputo, N., Fraser, R., Haber, J., Shields, C., Menke, M. N.: A retrospective analysis of the utility of an artificial neural network to predict ED volume. The American Journal of emergency medicine 32(6), 614-617 (2014).
[15]. Jiang, S., Chin, K. S., & Tsui, K. L.: A universal deep learning approach for modeling the flow of patients under different severities. Computer Methods and Programs in Biomedicine 154, 191-203 (2018).
[16]. Europarl Homepage, https://health.ec.europa.eu/system/files/2020-12/2020_healthatglance_rep _en_0, last accessed 2020/12.
[17]. Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., Sarris, J.: The efficacy of smartphone‐based mental health interventions for depressive symptoms: a meta‐analysis of randomized controlled trials. World Psychiatry 16(3), 287-298 (2017).
[18]. Mohr, D. C., Riper, H., Schueller, S. M.: A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA psychiatry 75(2), 113-114 (2018).
[19]. Clay, H., Stern, R.: Making time in general practice. Primary Care Foundation, 1-83 (2015).
[20]. Adamson, A. S., Smith, A.. Machine learning and health care disparities in dermatology. JAMA dermatology 154(11), 1247-1248 (2018).
[21]. Alder, S.: AI company exposed 2.5 million patient records over the internet. HIPAA Journal, (2020).
[22]. Hocking, L., Parks, S., Altenhofer, M., Gunashekar, S.: Reuse of health data by the European pharmaceutical industry, (2019).
[23]. Farina, R., Sparano, A.: Errors in sonography. Errors in radiology, 79-85 (2012).
[24]. Gillespie, N., Lockey, S., Curtis, C.: Trust in artificial intelligence: A five country study, (2021).
[25]. Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., Poon, D. S. Atti-tudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into imaging 11, 1-6(2020).
[26]. Yu, K. H., Kohane, I. S.: Framing the challenges of artificial intelligence in medicine. BMJ Quality & Safety 28(3), 238-241 (2019).
[27]. Wang, H. E., Landers, M., Adams, R., Subbaswamy, A., Kharrazi, H., Gaskin, D. J., Saria, S. A bias evaluation checklist for predictive models and its pilot application for 30-day hospital re-admission models. Journal of the American Medical Informatics Association 29(8), 1323-1333 (2022).
[28]. De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Ronneberger, O.: Clinically applicable deep learning for diagnosis and referral in retinal dis-ease. Nature Medicine 24(9), 1342-1350 (2018).
[29]. Von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Peltonen, L. M.: Artificial Intelligence-based technologies in nursing: A scoping literature review of the evidence. International Journal of nursing studies, 127, 104153 (2022).
[30]. Mora-Cantallops, M., Sánchez-Alonso, S., García-Barriocanal, E., Sicilia, M. A.. Traceability for trustworthy ai: A review of models and tools. Big Data and Cognitive Computing 5(2), 20 (2021).
[31]. Vyas, D. A., Eisenstein, L. G., Jones, D. S.: Hidden in plain sight-reconsidering the use of race correction in clinical algorithms. New England Journal of Medicine 383(9), 874-882 (2020).
Cite this article
Zhang,H. (2023). Artificial intelligence in healthcare: Opportunities and challenges. Theoretical and Natural Science,21,130-134.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Biological Engineering and Medical Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).