Revolutionizing healthcare: the role of artificial intelligence in clinical practice

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Abstract

Introduction

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

Research Significance

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI’s significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

Materials and Methods

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

Results

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

Conclusion

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

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Most cited references 112

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Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

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High-performance medicine: the convergence of human and artificial intelligence

Eric Topol (2019)

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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Machine learning: Trends, perspectives, and prospects.

M. I. Jordan, T. M. Mitchell (2015)

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

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Author and article information

Contributors

Shuroug A. Alowais: owaiss@ksau-hs.edu.sa

Journal

Journal ID (nlm-ta): BMC Med Educ

Journal ID (iso-abbrev): BMC Med Educ

Title: BMC Medical Education

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6920

Publication date (Electronic): 22 September 2023

Publication date PMC-release: 22 September 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 689

Affiliations

[1 ]Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, ( https://ror.org/0149jvn88) Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611 Saudi Arabia

[2 ]King Abdullah International Medical Research Center, ( https://ror.org/009p8zv69) Riyadh, Saudi Arabia

[3 ]GRID grid.415254.3, ISNI 0000 0004 1790 7311, Pharmaceutical Care Department, , King Abdulaziz Medical City, National Guard Health Affairs, ; Riyadh, Saudi Arabia

[4 ]Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, ( https://ror.org/0149jvn88) Riyadh, Saudi Arabia

Article

Publisher ID: 4698

DOI: 10.1186/s12909-023-04698-z

PMC ID: 10517477

PubMed ID: 37740191

SO-VID: dec912e7-cd02-44ff-8c78-966517eb5551

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 June 2023

Date accepted : 19 September 2023

Custom metadata

ScienceOpen disciplines: Education

Keywords: ai,healthcare,patient care,quality of life,clinicians,decision-making,personalized treatment plans

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ScienceOpen disciplines: Education

Keywords: ai, healthcare, patient care, quality of life, clinicians, decision-making, personalized treatment plans

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

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Abstract

Introduction

Research Significance

Materials and Methods

Results

Conclusion

Related collections

Artificial Intelligence in Medicine

Most cited references 112

Dermatologist-level classification of skin cancer with deep neural networks

High-performance medicine: the convergence of human and artificial intelligence

Machine learning: Trends, perspectives, and prospects.

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