ReviewInternational journal of molecular sciences2021
Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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.
- Article
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
- The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance.NPJ digital medicine · 2025Article
- MuTATE: an interpretable multi-endpoint machine learning framework for automated molecular subtyping in cancer.npj health systems · 2025Article
- Mapping the Advanced-Stage Epithelial Ovarian Cancer Landscape Goes Beyond Words: Two Large Language Models, Eight Tasks, One Journey.Journal of clinical medicine · 2025Article
- Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients.Schizophrenia (Heidelberg, Germany) · 2025Article
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Article
- Machine learning approach for the detection of vitamin D level: a comparative study.BMC medical informatics and decision making · 2023Article
- MuTATE-an R package for comprehensive multi-objective molecular modeling.Bioinformatics (Oxford, England) · 2023Article
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 2023Article
- AI and professional liability assessment in healthcare. A revolution in legal medicine?Frontiers in medicine · 2023Review
- Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study.International journal of clinical practice · 2023Article
- Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review.NPJ digital medicine · 2022Review
- Review
- Article
- Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".International journal of molecular sciences · 2022Article
- Vitamin D Deficiency in Women with Breast Cancer: A Correlation with Osteoporosis? A Machine Learning Approach with Multiple Factor Analysis.Nutrients · 2022Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.
Indexed as
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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.