ReviewJournal of personalized medicine2021
How Do Machines Learn? Artificial Intelligence as a New Era in Medicine.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Machine Learning and Smart Devices for Diabetes Management: Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Article
- AI in variant analysis: fast track to genetic diagnoses.Human genetics · 2026Review
- Virtual immunohistochemistry by conditional generative adversarial networks.Scientific reports · 2026Article
- Predicting Prostate Cancer Risk and Its Associated Factors Using Machine Learning Techniques: A Retrospective Study.Health science reports · 2026Article
- Personalized Prediction of Postoperative Recurrence in Lung Squamous Cell Carcinoma: Integrating AI-Based Nuclear Morphometry and Clinical Data.Journal of personalized medicine · 2026Article
- Genetic determinants of metabolic-inflammatory dysregulation and machine learning prediction of COVID-19.Frontiers in cellular and infection microbiology · 2026Article
- Article
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence technology for the ethical issues research from a Marxist perspective under deep learning.Scientific reports · 2025Article
- Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies.Scientific reports · 2025Article
- Artificial intelligence as team member versus manual screening to conduct systematic reviews in medical sciences.iScience · 2025Article
- Multiscale Fourier transform multiple instance learning for the Gleason grading of prostate cancer from whole-slide images.Quantitative imaging in medicine and surgery · 2025Article
- An interpretable hybrid deep learning framework for gastric cancer diagnosis using histopathological imaging.Scientific reports · 2025Article
- A dual-stream deep learning framework for skin cancer classification using histopathological-inherited and vision-based feature extraction.Scientific reports · 2025Article
- Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Development of a prognostic risk model for predicting biochemical recurrence-free survival in patients with prostate cancer based on lysine acetylation.Translational andrology and urology · 2025Article
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.