ReviewCureus2024
Deep Learning Approaches for Medical Image Analysis and Diagnosis.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolution or routine? Comparing AI and traditional imaging in thoracic surgery outcomes: a systematic review.Journal of medicine and life · 2025Pooled it
- Deep Learning-Assisted Classification of Urinary Red Blood Cell Morphology for Glomerular Hematuria Screening: A Pilot Study.Journal of clinical laboratory analysis · 2026Article
- CNN Sample-Size Effects Across Biomedical Datasets: A Reliability Pattern in Overfitting, Ranking, and Monotonicity.Bioengineering (Basel, Switzerland) · 2026Article
- Smart medical system integrating clinical workflows for robust skin cancer detection across heterogeneous pathologies.Scientific reports · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- An Automated Hybrid Deep Learning-based Model for Breast Cancer Detection using Mammographic ImagesCurrent medical imaging · 2026Article
- The capability of deep-radiomics to predict pathological response to neoadjuvant immunochemotherapy in non-small cell lung cancer: a retrospective multicenter study.Frontiers in immunology · 2026Article
- Machine Learning Models Utilizing Oxidative Stress Biomarkers for Breast Cancer Prediction: Efficacy and Limitations in Sentinel Lymph Node Metastasis Detection.Biomedicines · 2025Article
- Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach.NPJ digital medicine · 2025Article
- Review
- An Innovative Medical Image Analyzer Incorporating Fuzzy Approaches to Support Medical Decision-Making.Medical sciences (Basel, Switzerland) · 2025Article
- Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study.Scientific reports · 2025Article
- The impact of uncertainty estimation on radiomic segmentation reproducibility and scan-rescan repeatability in kidney MRI.Medical physics · 2025Article
- Automated Risser Grade Assessment of Pelvic Bones Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility.BMC medical education · 2025Article
- Combining shallow and deep neural networks on pseudo-color enhanced images for digital breast tomosynthesis lesion classification.Frontiers in digital health · 2025Article
- Deep-learning-based automatic liver segmentation using computed tomography images in dogs.Frontiers in veterinary science · 2025Article
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
In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way for seamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefit from deep learning models.
Indexed as
Identifiers
What Socratic 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 Socratic graph.