ReviewNature medicine2019
A guide to deep learning in healthcare.
Review in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 1,608 papers, 6 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.
Research on Intelligent Optimization of Ventilator Parameters for Intensive Care Patients Based on Multimodal Large Models
Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study
Who cites it
1,608 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.Evidence-based dentistry · 2026Pooled it
- Artificial intelligence in urological education: a systematic review.BMC medical education · 2026Pooled it
- Cost-effectiveness of artificial intelligence interventions for musculoskeletal disorders of the spine: a systematic review.Rheumatology international · 2026Pooled it
- Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Co-research in the development of AI and digital health tools for cancer management and care: a systematic review.BMC health services research · 2026Pooled it
- Symptom-based diagnostic models for common respiratory viral infections: a machine learning and natural language processing study.Infectious Disease Modelling · 2026Article
- Development and usability testing of a health recommender system for symptom management in women with breast cancer who were receiving or had recently received chemotherapy.Asia-Pacific journal of oncology nursing · 2026Article
- Between the hype and harm: does artificial intelligence in health offer solace or further exclusion for marginalised populations in Sub-Saharan Africa? A scoping review.Global health action · 2026Article
- Metagenomic-driven predictive biosafety and contamination traceability in stem cell manufacturing: Translating outbreak genomics into regenerative medicine.Regenerative therapy · 2026Review
- Biophotonics point-of-care diagnostics in low-resource settings: a South African perspective.Journal of biomedical optics · 2026Review
- Habitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A Dual-center Study with External Validation.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Prediction and Diagnosis of Fetal Growth Restriction: A Systematic Review.Indian journal of pediatrics · 2026Review
- Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.Abdominal radiology (New York) · 2026Article
- Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts.Journal of general internal medicine · 2026Article
- Can large language models accurately compute descriptive statistics from structured datasets? A comparative evaluation of ChatGPT and Claude.American heart journal plus : cardiology research and practice · 2026Article
- Scientific culture in the age of AI.Nature cell biology · 2026Article
- Artificial intelligence in cardiovascular medicine: a practical guide for clinicians and researchers.European heart journal. Digital health · 2026Review
- From voxels to models: a multi-center voxel-based morphometry study identifying robust biomarkers for deep learning classification of schizophrenia.Brain imaging and behavior · 2026Article
- Medical AI and Human Dignity: A Catholic Normative Ethical Framework in Light of Magnifica Humanitas.Journal of religion and health · 2026Article
1,548 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.
Indexed as
Identifiers
30617335What 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.