ArticleNPJ digital medicine2025
Large language models forecast patient health trajectories enabling digital twins.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Article
- Generative AI and Clinicians Show Comparable Prognostic Reasoning From Clinical Narratives in Biologic-Treated CRSwNP.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Large language models are powerful electronic health record encoders.NPJ digital medicine · 2026Article
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026Review
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Context defines precision: rethinking KRAS inhibition in oncology.NPJ precision oncology · 2026Review
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Host-microbiome-immune disequilibrium in oral disease: mechanisms, dysbiosis, and precision therapeutics.Frontiers in immunology · 2026Review
- Integration, challenges, and future of artificial intelligence in critical care medicine: comprehensive applications from predictive models to clinical integration.Frontiers in medicine · 2026Review
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- A hybrid ML-PBPK digital twin framework for clinically interpretable readmission risk and drug exposure stratification in diabetes.Frontiers in digital health · 2026Article
- Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis.Frontiers in digital health · 2026Article
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
- Article
- Large Language Models and Their Applications in Drug Discovery and Development: A Primer.Clinical and translational science · 2025Review
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Generative artificial intelligence is revolutionizing digital twin development, enabling virtual patient representations that predict health trajectories, with large language models (LLMs) showcasing untapped clinical forecasting potential. We developed the Digital Twin-Generative Pretrained Transformer (DT-GPT), extending LLM-based forecasting solutions to clinical trajectory prediction. DT-GPT leverages electronic health records without requiring data imputation or normalization and overcomes real-world data challenges such as missingness, noise, and limited sample sizes. Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer's disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively. It maintained distributions and cross-correlations of clinical variables, and demonstrated explainability through a human-interpretable interface. Additionally, DT-GPT's ability to perform zero-shot forecasting highlights potential advantages of LLMs as clinical forecasting platforms, proposing a path towards digital twin applications in clinical trials, treatment selection, and adverse event mitigation.
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.