The practical implementation of artificial intelligence technologies in medicine
Open accessAbstract
The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
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The increasing availability of digital health data, advances in computational capacity, and progress in algorithm development have accelerated AI integration into clinical practice.
“The increasing availability of digital health data, advances in computational capacity, and progress in algorithm development have accelerated its integration into clinical practice [3].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [3]
The current status of AI in medicine section describes easier access to health data, but says that substantial implementation of AI in clinical use had not yet occurred. The Abstract likewise distinguishes rapidly advancing AI technologies from their limited implementation in patient-care settings, contrary to the claim that these developments had accelerated integration into clinical practice. No page numbers are available in our copy. (LLM, conf=0.87)
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Experimental — accuracy not yet measuredAn AI tool's effectiveness in improving clinical outcomes depends on its adoption by front-line clinicians.
“An AI tools' effectiveness in improving clinical outcomes depends on its adoption by front-line clinicians, where “the rubber meets the road.”12”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [12]
The 'Education of an AI-literate workforce' section says stakeholders must be involved in implementation and healthcare providers need AI knowledge to work effectively on human–machine teams. 'Future directions' describes improved outcomes as a promise of these technologies. The paper does not specifically establish that improved clinical outcomes depend on adoption by front-line clinicians. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn general, AI tools have shown promise in development, but few have been able to translate into real-world settings for patient management.
“In general, AI tools have shown promise in development, but few have been able to translate into the real-world settings for patient management [4].”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [4]
Productizing AI algorithms for clinical use is an exceptionally complex task.
“Beyond developing AI algorithms, productizing them for clinical use is an exceptionally complex task4,13.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [4]
AI adoption in healthcare is expected to grow, but its actual implementation currently remains limited due to a variety of challenges.
“While AI adoption is expected to grow, its actual implementation currently remains limited due to a variety of challenges.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [4]
There is a global consensus that AI solutions must be fair and nondiscriminatory and that AI solutions in health care should have a positive impact across all sectors of social and economic life.
“There is a global consensus that AI solutions must be fair and nondiscriminatory and that AI solutions in health care should have a positive impact across all sectors of social and economic life.2,14,15”
From MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. (2020) · cites this paper as [2]
AI-related studies consider the safety and quality of autonomous operations an essential factor affecting the use of AI devices.
“AI-related studies consider the safety and quality of autonomous operations an essential factor affecting the use of AI devices [27].”
From Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020) · cites this paper as [27]
The decade 2012-2022 can be viewed as a stabilization phase for AI applications in oncology, marked by incremental improvements and specialized uses, predominantly in image analysis.
“This decade can be viewed as a stabilization phase for AI applications in oncology, marked by incremental improvements and specialized uses, predominantly in image analysis5.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [5]
Researchers in healthcare can use Machine Learning and Artificial Intelligence to create various designs and assess the practicality without endangering the lives of human volunteers.
“Researchers in healthcare can use Machine Learning and Artificial Intelligence to create various designs and assess the practicality without endangering the lives of human volunteers [131,132].”
From Medical 4.0 technologies for healthcare: Features, capabilities, and applications (2022) · cites this paper as [131]
Implementation of AI in clinical radiology practice is complex and has so far been slow.
“While technical performance of AI applications is expected to increase continuously, its implementation in clinical radiology practice is rather complex and has so far been slow [6, 12, 13].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [6]
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- Authors
- He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K.
- Affiliations
- First Affiliated Hospital of Guangzhou Medical University (CN)
- University of California San Diego (US)
- Beijing Tongren Hospital (CN)
- Fudan University (CN)
- Corresponding author
- He, Jianxing
- Zhang, Kang
- Venue
- Nature Medicine
- Published
- 2019-01
- Volume
- 25
- Issue
- 1
- Pages
- 30-36
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC6995276
- OpenAlex
- W2906295032
- Semantic Scholar
- d55e70a42579c7895938b6c43736163dbaf55145
- Cited by
- 2,246
- References
- 24
- Citations by year
- 2026: 308 · 2025: 420 · 2024: 395 · 2023: 356 · 2022: 282 · 2021: 251 · 2020: 164 · 2019: 69 · 2012: 1
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Guangzhou Medical University
- Guangzhou University
- Guangzhou Regenerative Medicine and Health Guangdong Laboratory
- U.S. National Library of Medicine (T15LM011271)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, clinical implementation, clinical workflow, data sharing, data privacy, algorithmic transparency, data standardization, interoperability, patient safety, AI regulation, health policy
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Medicine, Reference Standards, Social Control, Formal, United States
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