ReviewActa neurochirurgica2024
A critical moment in machine learning in medicine: on reproducible and interpretable learning.
Review in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 48 citations in OpenAlex.
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- Interpretable Deep Learning of Molecule-Solvent Interactions Using a Solvatochromic Subgroup Contribution Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- [Development and validation of an explainable machine learning-based model for predicting mortality risk in polytrauma patients].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms.Neurosurgery · 2026Article
- A generalizable computational framework for integrating heterogeneous biomarkers into interpretable scalar risk representations.BMC medical informatics and decision making · 2026Article
- Explainable Boosting Machine Predicting Length of Stay After Liver Surgery in Patients with Colorectal Liver Metastases.Cancers · 2026Article
- i2b2-ML: module to facilitate machine learning in the informatics for integrating biology and the bedside platform.JAMIA open · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Prioritising Data Quality Governance for AI in Prostate Cancer: A Methodological Proof-of-Concept Study Using Neural Networks for Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Article
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
- Interpretable predictive model for deterioration of kidney function in patients with stage 4 cardiovascular-kidney-metabolic syndrome.BMC nephrology · 2026Article
- Standardised Transparent Orthopaedic Reporting and Modelling for AI (STORM-AI)-Guidelines for reporting artificial intelligence studies in orthopaedics from the ESSKA AI Working Group.Journal of experimental orthopaedics · 2026Article
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- Machine learning models for predicting postoperative paraplegia in acute type A aortic dissection patients.Journal of thoracic disease · 2026Article
- Article
- Is orthopaedics entering the age of generative AI?-A narrative review of current applications challenges and future directions.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Review
- Multiverse analysis of machine learning: classification between groups defined by suicidal ideation screening status using acoustic features in college students.Frontiers in psychology · 2026Article
- ATF-MGIAM: Medically-guided interpretable attention mapping for robust pertussis cough sound recognition.PloS one · 2026Article
- Machine learning-based prediction of prolonged length of stay in older patients with type 2 diabetes mellitus and cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.
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.