mlsanityData to 14 Oct 2026

Artificial Intelligence for In Silico Clinical Trials: A Review

arXiv 2209.09023 ↗First posted 16 Sep 2022q-bio.QM cs.AI cs.LGcited by 6

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Abstract (arXiv)

A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 39 of its 108 references that are in the map. First posted on arXiv 16 Sep 2022.

Builds on

39 of 108 references.

References · 39

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27 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.

6 papers.

  1. 2022PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learningcited by 22
  2. 2024Uncertainty Quantification on Clinical Trial Outcome Predictioncited by 19
  3. 2023SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learningcited by 13
  4. 2023PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applicationscited by 3
  5. 2024CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Modelscited by 2
  6. 2025SECRET: Semi-supervised Clinical Trial Document Similarity Searchcited by 2