ReviewEBioMedicine2019
Looking beyond the hype: Applied AI and machine learning in translational medicine.
Review in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis 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
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review: Drug Repositioning for Congenital Disorders of Glycosylation (CDG).International journal of molecular sciences · 2022Pooled it
- Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning.Frontiers in immunology · 2023Trial
- Acute medicine 2050: A connected future of care.Future healthcare journal · 2026Article
- Toward Precision Health in Autoimmunity and Immune-Related Adverse Events: The Autoantibody Reactome, Spatial Omics, and Multimodal Data Integration.Biomedicines · 2026Article
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- Diagnostic and prognostic significance of circulating extracellular vesicles in gastrointestinal cancers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Multi-omics and machine learning-based exploration of key genes associated with abdominal aortic aneurysm.Frontiers in molecular biosciences · 2026Article
- A machine learning integrated multi-omics framework for risk prediction and target discovery in insomnia aggravated sepsis induced acute lung injury.Frontiers in immunology · 2026Article
- A Machine Learning Model Based on Clinical Factors to Predict the Efficacy of First-Line Immunochemotherapy for Patients With Advanced Gastric Cancer: Retrospective Study.JMIR medical informatics · 2025Article
- "Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification".BMC cancer · 2025Article
- Mapping antimicrobial resistance and healthcare-associated infections indicators for actionable benchmarking: a cross-network overview in a data-driven era.Antimicrobial resistance and infection control · 2025Review
- Using a consensus machine learning approach to identify host biomarkers associated with treatment response in pulmonary tuberculosis.Briefings in bioinformatics · 2025Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- Identification of novel biomarkers and drug targets for frailty-related skeletal muscle aging: a multi-omics study.QJM : monthly journal of the Association of Physicians · 2025Article
- Using Machine Learning Methods to Develop Diagnostic and Prognostic mRNA Signatures for Pancreatic Cancer in Plasma Small Extracellular Vesicles.Digestive diseases and sciences · 2025Article
- Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients.NPJ precision oncology · 2025Article
- A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025Review
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review.JAMIA open · 2025Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
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.