# Artificial Clinic Intelligence (ACI): A Generative AI-Powered Modeling Platform to Optimize Patient Cohort Enrichment and Clinical Trial Optimization

## TL;DR

This paper introduces ACI, a generative AI platform that optimizes clinical trials by predicting which patients will benefit most from a drug.

## Contribution

ACI is a novel generative AI framework for virtual clinical trial enrichment using synthetic patient data and drug response modeling.

## Key findings

- ACI generates synthetic patient data to model drug responses across diverse populations.
- The platform ranks clinical attributes that influence drug sensitivity for better patient selection.
- ACI helps clinicians identify key parameters for prospective patient recruitment before trial accrual.

## Abstract

Clinical trials are critical for assessing drug efficacy, toxicity, and potential long-term health effects in humans. Identifying in advance which patients are likely to benefit from a trial drug can enhance the success rate of clinical trials while reducing resource allocation and shortening the time for drug approval. In this perspective article we offer a roadmap on how to achieve Artificial Clinic Intelligence (ACI), a generative AI-driven virtual clinical trial enrichment framework which implements novel concepts for streamlining the selection of patients that will drive the most clinical benefits from a test drug. ACI generates synthetic patient data, models drug response across clinically diverse populations, and ranks the importance of clinical attributes underlying drug sensitivity. By evaluating the extent to which a prospective patient will benefit from a drug, ACI helps drive the success of clinical trials.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.