ArticleNature reviews bioengineering2026
Transparency of medical artificial intelligence systems.
Article in Nature reviews bioengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- The IMAGE Statement: A Proposed Reporting Guideline for Clinical Images-A CARE Guideline Extension.Methods and protocols · 2026Article
- Advancements in CRISPR/Cas Technologies for Sensitive Cancer Detection: Mechanisms, Platforms, and Clinical Translation Roadmap.Diagnostics (Basel, Switzerland) · 2026Review
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Systemic fragility in European total intravenous anesthesia delivery and opportunities for resilient real-time decision support.Communications medicine · 2026Review
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- Implementing Digital Respiratory Technologies for People With Respiratory Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025).Pharmaceutics · 2026Review
- Biosensing technologies for foodborne pathogen detection and healthcare: principles, emerging materials, and intelligent platforms.Mikrochimica acta · 2026Review
- Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI.NPJ digital medicine · 2026Article
- Materials and System Design for Self-Decision Bioelectronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.JACC. Advances · 2026Article
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- A generative two-stage semantic intermediary framework for explainable mental health early warning in higher education.Frontiers in psychiatry · 2026Review
- Artificial intelligence in gangrenous cholecystitis: advances in risk factor identification and preoperative prediction-a scoping review.Frontiers in surgery · 2026Review
- Organ-on-a-chip platforms for disease modeling and in vitro diagnostic applications.Frontiers in bioengineering and biotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Artificial intelligence (AI) systems are now prevalent in our daily lives and hold promise for transforming high-stakes fields such as healthcare. Medical AI systems are showing significant potential to support diagnostics and treatment recommendations. As these systems play an increasingly significant role in clinical decision-making, ensuring transparency in their design, operation, and outcomes is essential for building trust among key stakeholders, including patients, providers, developers, and regulators. However, many systems still function as "black boxes," making it challenging for users-such as clinicians, patients, and other stakeholders-to interpret and verify their inner workings. Here, we examine the current state of transparency in medical AIs, identifying key challenges and risks these opaque systems pose. After motivating the need for transparency in all aspects of the machine learning pipeline, from training data to model development to model deployment, we explore a range of techniques that promote explainability throughout the pipeline while highlighting the importance of continual monitoring and system updates to ensure that AI systems remain reliable over time. Finally, we address the need to overcome barriers that inhibit the integration of transparency tools into clinical settings and review regulatory frameworks that prioritize transparency in emerging AI systems. Through this survey, we aim to increase awareness of current challenges and offer actionable insights for stakeholders, such as researchers, clinicians, and regulators, on how to build trustworthy and ethically responsible AI healthcare solutions.
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.