GPT can read and understand how to utilize any well-documented modern API. It also gives users the ability to train the learning model on new specialized or niche interactions. GPT understands FHIR. How far are we, really, from a fine-tuned GPT model analyzing health data to produce its own research insights, or assisting physicians in the analysis of medical images?

The key to faster patient recruitment, improved retention rates, greater diversity among patient populations, and better capability to study rare conditions is to ensure that participating in medical research is a humanized and accessible experience.

We will cover some of the technical and institutional challenges that arise when shifting into eSource from traditional paper trials.

The justification for designing eSource trials is in the advantages they provide in reliability across most human tasks that interface with a clinical trial database. In short, eSource as a strategy eliminates a broad spectrum of potential transcription errors in addition to fully leveraging the supporting eClinical technology to provide reliable consistent exchange of information.

Adoption of risk-based monitoring practices has grown in the industry of clinical trials at a slow but steady pace.