ReviewComputational and structural biotechnology journal2024
From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses 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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.Journal of patient-reported outcomes · 2024Pooled it
- Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
- Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- MedDiscover: A Domain-Specific Retrieval-Augmented Generation Framework for Evidence-Grounded Knowledge Extraction in Metabolomics.Computational and structural biotechnology journal · 2026Article
- Large language models in adolescent suicide prevention: from language signals to accountable action.Frontiers in public health · 2026Review
- From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions.Computational and structural biotechnology journal · 2026Article
- ATF-MGIAM: Medically-guided interpretable attention mapping for robust pertussis cough sound recognition.PloS one · 2026Article
- Screening anxiety via contrastive autobiographical recall.Frontiers in digital health · 2026Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Machine learning and deep learning to improve overall survival prediction in cervical cancer patients.Translational cancer research · 2025Article
- Advancing breast, lung and prostate cancer research with federated learning. A systematic review.NPJ digital medicine · 2025Article
- Explainable AI for suicide risk detection: gender- and age-specific patterns from real-time crisis chats.Frontiers in medicine · 2025Article
- Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review.Mass spectrometry (Tokyo, Japan) · 2025Review
- Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.Sensors (Basel, Switzerland) · 2024Review
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
Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term "eXplainable and Interpretable Artificial Intelligence" (XIAI) is introduced to distinguish XAI from IAI. Different models are further categorized based on their functionality (model-, input-, output-based) and scope (local, global). Our analysis shows that attention mechanisms are the most prevalent emerging IAI technique. The use of IAI is growing, distinguishing it from XAI. The major challenges identified are that most XIAI does not explore "global" modelling processes, the lack of best practices, and the lack of systematic evaluation and benchmarks. One important opportunity is to use attention mechanisms to enhance multi-modal XIAI for personalized medicine. Additionally, combining DL with causal logic holds promise. Our discussion encourages the integration of XIAI in Large Language Models (LLMs) and domain-specific smaller models. In conclusion, XIAI adoption in healthcare requires dedicated in-house expertise. Collaboration with domain experts, end-users, and policymakers can lead to ready-to-use XIAI methods across NLP and medical tasks. While challenges exist, XIAI techniques offer a valuable foundation for interpretable NLP algorithms in healthcare.
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.