ReviewBriefings in bioinformatics2018
Deep learning for healthcare: review, opportunities and challenges.
Review in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 719 papers, 12 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
719 citing papers in PubMed, 12 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Artificial intelligence-supported therapeutic interventions for autism spectrum disorder: a systematic review.Frontiers in psychiatry · 2026Pooled it
- Economic evaluation of AI-based oral disease screening: a systematic review.Frontiers in medical technology · 2026Pooled it
- Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review.JMIR aging · 2025Pooled it
- Representation of Rural Older Adults in AI for Health Research: Systematic Literature Review.JMIR human factors · 2025Pooled it
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare.Hormones (Athens, Greece) · 2025Pooled it
- Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Integrating Artificial Intelligence (AI) With Workforce Solutions for Sustainable Care: A Follow Up to Artificial Intelligence and Machine Learning (ML) Based Decision Support Systems in Mental Health.International journal of mental health nursing · 2025Pooled it
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics.Seminars in orthodontics · 2026Article
- Improved diffusion tensor parametric maps in the brain from reduced-direction acquisitions using a deep learning approach.Magma (New York, N.Y.) · 2026Article
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Artificial intelligence applications in out-of-hospital cardiac arrest: an analysis of emerging research trends.International journal of emergency medicine · 2026Review
- Review
- Hierarchical contrastive learning from GP-recorded and eGFR labels for CKD staging.Medical & biological engineering & computing · 2026Article
- A Secure, Scalable Large Language Model-Based System (CIDER) for High-Throughput Clinical Data Extraction From Medical Reports: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Enhanced YOLOv8 with Hybrid Attention Gating Block for Intertrochanteric Fracture Detection in Complex X-Ray Images.Journal of imaging informatics in medicine · 2026Article
659 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
5 authors.
Funding
Abstract
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.
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.