Artificial Intelligence and Machine Learning Applied at the Point of Care
Open accessAbstract
INTRODUCTION: The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. OBJECTIVE: Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. METHODS: A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. RESULTS: From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. CONCLUSIONS: The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.
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- Authors
- Angehrn, Z., Haldna, L., Zandvliet, A. S., Gil Berglund, E., Zeeuw, J., Amzal, B., Cheung, S. Y. A., Polasek, T. M., Pfister, M., Kerbusch, T., & Heckman, N. M.
- Affiliations
- Certara (United States) (US)
- Royal Adelaide Hospital (AU)
- Corresponding author
- Zandvliet, Anthe S.
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2020-06-18
- Volume
- 11
- Pages
- 759-759
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC7314939
- OpenAlex
- W3030588587
- Cited by
- 93
- References
- 39
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 27 · 2023: 10 · 2022: 16 · 2021: 10 · 2020: 1
- FWCI
- 0.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence in healthcare, machine learning, point of care, software as a medical device, regulatory requirements, precision dosing, clinical decision support, software risk analysis, external validation, data privacy, implementation barriers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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