The use of AI can help improve trial efficiency in various ways said Danish Mairaj, principal engineer of medical device design at Resyca.
Mairaj cited different ways the AI can be used – for efficient trial design, increasing patient recruitment and data analysis.
The various methods that can be employed to accelerate clinical trials for medical devices, including the use of AI and RWD were at the forefront of the Outsourcing in Clinical Trials: Medical Devices Europe 2023 conference in Munich, Germany, on 30-31 January.
Mairaj highlighted that the current process for obtaining regulatory approval for a medical device was long and arduous, adding that the use of AI can not only decrease the time for gaining approval but can also improve the current 10% success rate.
In addition, Ben McConnochie, director of strategic development at Acron AI (Medidata) talked about how the addition of RWD to supplement the clinical trial data can also help decrease the trial cost. Adding that the RWD can be used to prove extended follow-up data.
McConnochie also stated that RWD can help plug gaps in clinical trial data, citing the example of how PRD can be used to track outcomes for patients lost to follow-ups. He added that the use of RWD also lowers the cost of data generation whilst providing trial sponsors access to longer follow-up data.
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He cautioned that privacy concerns and patients’ identifiable data need to be considered when linking RWD with clinical trial data. Adding centralisation and deidentification of this data is important when linking RWD and clinical trial data to provide longitudinal data for the patient.
McConnochie highlighted three key factors to implement for RWD linkage – correct, explicit and informed patient consent, centralised data management and future-proofing RWD access. He added that, although this RWD and clinical trial data linkage started in the US, with the FDA currently providing related guidance, other countries all over the world are adopting this approach.
