THE ROLE OF AI IN TRANSFORMING CLINICAL TRIALS By Ka-Mei Au
Emerging technology and patient centricity are currently some of the biggest topics surrounding clinical research. Here we discuss some of the ways in which artificial intelligence (AI) can be utilised to transform key steps in clinical trials to increase clinical trial efficiency, improve patient safety and the patient experience. Enrolment:
matched against the eligibility criteria of ongoing trials. This level of precise automation allows eligible candidates to be identified within minutes rather than months and relies less on the patient to find suitable trials for themselves. With such high processing rates, more potential candidates can be identified and contacted, increasing clinical trial awareness and recruitment outreach to more diverse populations.
Among the many reasons why clinical trials are terminated, the single biggest contributor is insufficient patient intake, accounting for 55% of trial terminations [1]. This is followed by lack of efficacy which only accounts for 15% of terminated trials [1]. Doctors can recommend clinical trials they are aware of to their patients, but otherwise the onus is often on the patients to find clinical trials themselves. This could be by looking through government websites, medical research charities and seeking out patient recruiters for clinical studies. However, this can be overwhelming and time-consuming, especially for those who are unfamiliar with these resources and the complicated medical jargon. Finding suitable candidates with comparable confounders and underlying conditions from the very start of a trial is also important to identify safety issues as soon as possible. There is also a need to increase focus on recruiting more diverse patient populations in clinical studies in order to reduce the disparity in health outcomes between different patient ethnicities. Several barriers that prevent ethnic minorities from participating in clinical trials have been identified – one of them being a lack of clinical trial awareness [2]. AI tools can improve the enrolment process for both the patient and clinical researchers, and to help mitigate these current issues in healthcare.
Monitoring: A requirement for patients participating in clinical trials is having to regularly visit the research site for check-ins. These visits have traditionally been considered essential to enable investigators to assess
For example, natural language processing (NLP) is being used to extract and analyse patient health records in the US to find suitable study candidates for clinical trials [3]. The software can analyse structured and unstructured data, including doctor’s notes, pathology reports and other medical data in free-text form. All extracted data is used to form a unified patient graph which can then be 22
