ArticleDiagnostic and prognostic research2025
Why we do need explainable AI for healthcare.
Article in Diagnostic and prognostic research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
- Integrating single-cell transcriptomics with deep learning for glioblastoma treatment.Frontiers in genetics · 2026Review
- Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment.BMC medical informatics and decision making · 2024Article
- Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning.Nature communications · 2024Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- Review
- Interpretable early mortality prediction in oncology ICU patients: A dual-cohort validation of a LASSO-XGBoost-SHAP framework.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent uptake in certified Artificial Intelligence (AI) tools for healthcare applications has renewed the debate around their adoption. Explainable AI, the sub-discipline promising to render AI devices more transparent and trustworthy, has also come under scrutiny as part of this discussion. Some experts in the medical AI space debate the reliability of Explainable AI techniques, expressing concerns on their use and inclusion in guidelines and standards. Revisiting such criticisms, this article offers a balanced perspective on the utility of Explainable AI, focusing on the specificity of clinical applications of AI and placing them in the context of healthcare interventions. Against its detractors and despite valid concerns, we argue that the Explainable AI research program is still central to human-machine interaction and ultimately a useful tool against loss of control, a danger that cannot be prevented by rigorous clinical validation alone.
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.