ReviewJournal of the Royal Society, Interface2018
Opportunities and obstacles for deep learning in biology and medicine.
Review in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 801 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
801 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review.JMIR medical informatics · 2025Pooled it
- Proteomic Mendelian randomization and machine learning reveal causal plasma biomarkers in cardiorenal comorbidity.Renal failure · 2026Article
- Development and validation of venous thrombosis risk prediction model and scale for lung cancer patients in intensive care unit based on interpretable machine learning.Journal of thoracic disease · 2026Article
- From Marine Peptides to Oncology: The Therapeutic Potential of Conotoxins in Cancer Treatment.Chemistry & biodiversity · 2026Review
- Phase-dependent nutrient requirements of broiler breeder hens for day-old chick production in a field study using interpretable machine learning.Poultry science · 2026Article
- Artificial intelligence in knee osteotomy: evolution of alignment assessment and surgical decision-making.Journal of robotic surgery · 2026Review
- AI-Guided Long Non-Coding RNA Target Discovery for Precision Medicine: Integrating GWAS, Multi-Omics, Experimental Validation, and RNA Therapeutics.Biomedicines · 2026Review
- Special Issue "Machine Learning Applications in Bioinformatics and Biomedicine: 3rd Edition".International journal of molecular sciences · 2026Article
- Protein Language Model Embeddings Reveal Proteome-Scale Ortholog Divergence Relevant to Cross-Species Pharmacology.Proteomes · 2026Article
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Automated image analysis for intracellular ice detection in monolayers by freeze-substitution.Npj imaging · 2026Article
- Cross-domain transfer learning from peptides to metabolites using a multi-property fine-tuned LLM.Bioinformatics (Oxford, England) · 2026Article
- Clinical registry metadata as a hidden bottleneck in AI-driven drug discovery: a computational audit of translational phase data in glioma research.Scientific reports · 2026Article
- Artificial intelligence and the future of prosthodontics: a narrative review.The Saudi dental journal · 2026Article
- B.R.E.A.S.T. Breast canceR Enhanced AI-Supported Therapy: A New Interpretable Proteomics-Driven Machine Learning Framework for Therapy Response Prediction in Breast Cancer.International journal of molecular sciences · 2026Article
- Article
- MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models.bioRxiv : the preprint server for biology · 2026Article
741 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
36 authors.
Funding
Abstract
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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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.