Research
The science behind smarter trials
Our research teams publish on innovative AI methods and advanced disease modeling to support clinical development across therapeutic areas.
Therapeutic Areas
- Alzheimer’s disease
- Amyotrophic Lateral Sclerosis
- Asthma
- Atopic Dermatitis
- COPD
- Coronary Artery Disease
- Crohn’s disease
- Dyslipidemia
- Frontotemporal dementia
- Huntington's disease
- Hypertension
- Migraine
- Obesity
- Osteoarthritis
- Osteoporosis
- Parkinson's disease
- Psoriasis
- Psoriatic Arthritis
- Rheumatoid Arthritis
- SCI
- Stroke
- Type 2 Diabetes
- Ulcerative Colitis
Whitepapers
Deep dives from our team
- [Digital Twins
Defining Digital Twins in Drug Discovery and Development](https://www.unlearn.ai/forms/defining-digital-twins-in-drug-discovery-and-development) - [Trial Design
The Hidden Bill of Inefficient Clinical Trial Design](https://www.unlearn.ai/forms/whitepaper-download-the-hidden-bill-of-inefficient-clinical-trial-design) - [Regulatory
A Risk-Based Approach for Leveraging AI in Clinical Trials](https://www.unlearn.ai/forms/whitepaper-download-a-risk-based-approach-for-leveraging-ai-in-clinical-trials) - [Regulatory
How to Position Clinical Trials with Digital Twins for Regulatory Success](https://www.unlearn.ai/forms/how-to-position-clinical-trials-with-digital-twins-for-regulatory-success) - [AI & Machine Learning
A Fresh Approach to Precision Oncology Decision-Making](https://www.unlearn.ai/forms/whitepaper-download-a-fresh-approach-to-precision-oncology-decision-making) - [AI & Machine Learning
What AI Companies Need to Know About Clinical Development](https://244499163.fs1.hubspotusercontent-na2.net/hubfs/244499163/Unlearn%20AI%20in%20Clinical%20Development%20Whitepaper.pdf?utm_source=unlearn_website&utm_medium=referral&utm_campaign=clinical_dev_whitepaper) - [AI & Machine Learning
The Scientific Intelligence Layer for Connected Clinical Trial Decisions](https://www.unlearn.ai/forms/the-scientific-intelligence-layer-for-connected-clinical-trial-decisions)
Research Publications
Recent Articles
- [Date
May 2026
AI & Machine Learning
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies
Publisher
arXiv](https://arxiv.org/abs/2605.23246) - [Date
May 2026
AI & Machine Learning
FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence
Publisher
arXiv](https://arxiv.org/abs/2605.16246) - [Date
May 2026
AI & Machine Learning
Digital Twins as Synthetic Controls in Single-Arm Trials
Publisher
arXiv](https://arxiv.org/abs/2605.12832) - [Date
Jan 2025
AI & Machine Learning
Assessment of AI-generated digital twin methodology on reduction of treatment effect variance and potential clinical trial sample size saving using a Phase 2 trial dataset from patients with Alzheimer’s disease
Publisher
Alzheimer's Association](https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.090685) - [Date
May 2024
AI & Machine Learning
Digital Twin Generators for Disease Modeling
Publisher
arXiv](https://arxiv.org/abs/2405.01488) - [Date
Apr 2024
Clinical Research
Enhancing Longitudinal Clinical Trial Efficiency with Digital Twins and Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM)
Publisher
arXiv](https://arxiv.org/abs/2404.17576) - [Date
Nov 2023
Clinical Research
Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors
Publisher
arXiv](https://arxiv.org/abs/2310.18027) - [Date
Sep 2023
Clinical Research
Addressing Concerns About Digital Twins in Clinical Trials
Publisher
arXiv](https://arxiv.org/abs/2309.14256) - [Date
May 2023
AI & Machine Learning
Neural Boltzmann Machines
Publisher
arXiv](https://arxiv.org/abs/2305.08337) - [Date
Dec 2022
Clinical Research
Evaluating Digital Twins for Alzheimer’s Disease using Data from a Completed Phase 2 Clinical Trial
Publisher
Alzheimer’s Association](https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.065386) - [Date
Dec 2022
Clinical Research
Prognostic Covariate Adjustment for Binary Outcomes Using Stratification
Publisher
arXiv](https://arxiv.org/abs/2212.09903) - [Date
Aug 2022
Clinical Research
Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision
Publisher
arXiv](https://arxiv.org/abs/2208.04495) - [Date
Dec 2021
Clinical Research
Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
Publisher
De Gruyter](https://pubmed.ncbi.nlm.nih.gov/34957728/) - [Date
Dec 2020
AI & Machine Learning
Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
Publisher
arXiv](https://arxiv.org/abs/2012.13455) - [Date
Dec 2020
Clinical Research
Bayesian prognostic covariate adjustment
Publisher
arXiv](https://arxiv.org/abs/2012.13112) - [Date
Feb 2020
AI & Machine Learning
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Publisher
arXiv](https://arxiv.org/abs/2002.02779) - [Date
Sep 2019
AI & Machine Learning
Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
Publisher
Nature](https://www.nature.com/articles/s41598-019-49656-2) - [Date
May 2019
AI & Machine Learning
A high-bias, low-variance introduction to Machine Learning for physicists
Publisher
arXiv](https://arxiv.org/abs/1803.08823)
Blog
Our perspectives
- Decision Debt: The Cost Clinical Development Doesn't Track
- How SOLA Biosciences chose to design SOL-257 with digital twins from the start
- ProJenX's PRO-101 and the case for digital twins in single-arm ALS
Learn why leading biopharma sponsors trust our AI-powered products and solutions in their most important clinical programs.