5 things needed to transform oncology clinical trials
June 06, 2019

Transform Oncology Clinical Trial Prescreening With AI

If the requirements are met, an effective AI-enhanced prescreening tool will allow more principal investigators to run clinical trials and more patients to gain access to potentially lifesaving treatments.

Prescreening potential candidates is typically one of the most significant limiting factors in running oncology clinical trials. Identifying eligible participants requires manual effort and resources from trained medical professionals to flag prospects, scan their charts and confirm eligibility. The process creates delays, increases costs and burdens trials that cannot find enough appropriate patients to evaluate results.

While there has been significant progress in developing artificial intelligence (AI) tools for pulling information out of large data sets, no automated method for using an AI to assist in clinical trial prescreening has yet demonstrated its usefulness.

The reasons for the failure lie not only in the difficulty of developing the sophisticated algorithms required to deal effectively with unstructured clinical oncology data, but also in integrating the clinical AI into the procedures, practices, data flows and administrative requirements of a busy clinical setting.

Access the complete article.  was originally published by MedCity News on June 6, 2019.