eD octor: machine learning and the future of medicine
Open accessAbstract
Machine learning ( ML ) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer‐aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML , explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.
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Machine learning algorithms analyze extensive clinical data and identify underlying patterns and trends, offering robust support for disease prediction and risk assessment.
“ML algorithms, which analyze extensive clinical data and identify underlying patterns and trends, offer robust support for disease prediction and risk assessment [8, 9].”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [9]
AI offers untapped potential as a strong tool in biological research, in addition to acting as a eDoctor for illness diagnosis, management, and prognosis.
“AI offers untapped potential as a strong tool in biological research [127], in addition to acting as a eDoctor for illness diagnosis, management, and prognosis.”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [127]
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- Authors
- Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H.
- Affiliations
- Royal Victoria Hospital (GB)
- Northern Hospital (AU)
- Monash Health (AU)
- University of Ottawa (CA)
- Royal College of Surgeons in Ireland (IE)
- Deakin University (AU)
- Corresponding author
- Handelman, G. S.
- Venue
- Journal of Internal Medicine
- Publisher
- Wiley
- Published
- 2018-09-03
- Volume
- 284
- Issue
- 6
- Pages
- 603-619
- ISSN
- 0954-6820 (ISSN-L)
- 1365-2796
- OpenAlex
- W2886522935
- Cited by
- 956
- References
- 98
- Citations by year
- 2026: 142 · 2025: 210 · 2024: 214 · 2023: 159 · 2022: 92 · 2021: 99 · 2020: 23 · 2019: 15 · 2018: 1
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Monash University Research Portal (Monash University) (submittedVersion, other-oa)
- Deakin Research Online (Deakin University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion)
- 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 › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning, medical research, personalized medicine, computer-aided diagnosis, biomedical data, machine learning algorithms, future of medicine
- MeSH terms
- Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning, Algorithms, Forecasting, Humans, Medicine, Decision Support Systems, Clinical, Precision Medicine
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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