Demonstrating the efficacy at scale of a novel data-protecting AI system for surfacing clinically-eligible patients for clinical trials

**Issue**

**Clinical trial participant identification is desperately manual, time-consuming and frequently fails. As such, it is a well-known bottle-neck to healthcare R&D.**

**Clinical recruitment is a two-fold process: patient identification, followed by screening**. In order to identify patients with specific clinical presentation and attributes, pharmaceutical companies typically outsource to Contract Research Organisations (CRO) or Participant Identification Centres (PICs). CROs advertise widely, trawl through records manually and pay clinical experts to manually assess potential participant's fit. PICs and hospitals effectively wait for eligible patients to attend their consultations, whereupon inclusion in the trial is often forgotten! Consequently, patient identification using these inefficient methods takes months or years.

**New Technologies**

The last decade has seen an explosion in the use of machine learning and AI technologies across society. In the healthcare sector, these techniques have the potential to dramatically improve the quality and cost effectiveness of patient care. **This potential has been limited because healthcare data privacy is compromised in traditional machine learning**.

A new development in AI is **federated-machine-execution**. Instead of moving data to a central location for machine learning models to be applied or trained, algorithms are sent to the data. For a hospital, this ensures the privacy of the data and provides control and a complete audit trail of its usage. **The hospital's data never leaves its secure environment**.

**This project**

This project uses **Bitfount Ltd's federated-machine-execution platform** to enable **Moorfields Eye Hospital's** **cutting-edge software models** (AI-biomarkers) to scan millions of medical images and identify potential trial participants from this primary, objective source **swiftly and accurately**, leaving patient data and comprehensive lists of clinically-eligible trial candidates inside the hospital database. Our approach promises to massively increase the number of clinically-eligible participants suitable for screening and trial recruitment through **fast, accurate, cheap identification, without data leaving the hospital**. Its efficiency and efficacy will be measured via a Study Within a Trial (SWAT) by **Surrey CTU**, experts in AI Clinical Trials.

This project will demonstrate the approach in dry Age-related Macular Degeneration(AMD), which affects approximately 5% of Europeans aged 65 and over(39). Ophthalmology has the NHS' highest outpatient attendance rate(6) and AMD is by far the UK's leading cause of blindness(7), increasing with an ageing population. This project contributes to **NHSX's** digital transformation of ophthalmology, **BEIS' Grand Challenge for AI and data**(8), **DCMS' official Tech Priorities**(9), **UK's National AI Strateg**y(10) and **Life Sciences Industrial Strategy**(11).