ReviewFrontiers in genetics2021
Intelligent Health Care: Applications of Deep Learning in Computational Medicine.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis.Journal of translational medicine · 2024Pooled it
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.Animals : an open access journal from MDPI · 2026Review
- Article
- Boosting the Predictive Power of Virus-Specific T-Cell Clinical Trials via Generative Models.Computational and structural biotechnology journal · 2026Article
- Diagnostically competitive performance of a physiology-informed generative multi-task network for contrast-free CT perfusion.Frontiers in human neuroscience · 2026Article
- A review of image processing and analysis of computed tomography images using deep learning methods.Physical and engineering sciences in medicine · 2025Review
- The Right to Explanation in AI: In a Lonely Place.Journal of medical Internet research · 2025Article
- Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.Biomedicines · 2025Review
- Evaluation of deepseek, gemini, ChatGPT-4o, and perplexity in responding to salivary gland cancer.BMC oral health · 2025Article
- Overcoming translational barriers in RNA-protein docking: enhancing computational accuracy for targeted drug discovery.Future medicinal chemistry · 2025Review
- An Introduction to the Artificial Intelligence-Driven Technology Adoption in Nursing Education Conceptual Framework: A Mixed-Methods Study.Nursing reports (Pavia, Italy) · 2025Article
- Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients.Biomimetics (Basel, Switzerland) · 2025Article
- Challenges and advances in the management of inflammation in atherosclerosis.Journal of advanced research · 2025Review
- Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API.Frontiers in digital health · 2025Review
- Therapeutic gene target prediction using novel deep hypergraph representation learning.Briefings in bioinformatics · 2024Article
- Towards equitable AI in oncology.Nature reviews. Clinical oncology · 2024Review
- Artificial Intelligence Reporting Guidelines' Adherence in Nephrology for Improved Research and Clinical Outcomes.Biomedicines · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
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 the progress of medical technology, biomedical field ushered in the era of big data, based on which and driven by artificial intelligence technology, computational medicine has emerged. People need to extract the effective information contained in these big biomedical data to promote the development of precision medicine. Traditionally, the machine learning methods are used to dig out biomedical data to find the features from data, which generally rely on feature engineering and domain knowledge of experts, requiring tremendous time and human resources. Different from traditional approaches, deep learning, as a cutting-edge machine learning branch, can automatically learn complex and robust feature from raw data without the need for feature engineering. The applications of deep learning in medical image, electronic health record, genomics, and drug development are studied, where the suggestion is that deep learning has obvious advantage in making full use of biomedical data and improving medical health level. Deep learning plays an increasingly important role in the field of medical health and has a broad prospect of application. However, the problems and challenges of deep learning in computational medical health still exist, including insufficient data, interpretability, data privacy, and heterogeneity. Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.
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.