I am a pediatric cardiologist and Chief Intelligence and Innovation Officer of CHOC Sharon Disney Lund Medical Intelligence, Information, Investigation and Innovation Institute (Mi4). I am the founder of Artificial Intelligence in Medicine (AI-Med), the Medical Intelligence Society (MIS), the American Board of Artificial Intelligence in Medicine (ABAIM), the Alliance of Centers in Artificial Intelligence in Medicine (ACAIM) and the Pediatric Centers of Artificial Intelligence in Medicine (PCAIM). My book, Intelligence-Based Medicine is the first of its kind textbook on AI in medicine and is used at colleges and universities around the globe.
“It is a capital mistake to theorize before one has data.”
Sir Arthur Conan Doyle (Sherlock Holmes)
This timely editorial from JAMA on the current state of clinical trials points out that clinical research is in very desperate need of innovation in the form of artificial intelligence. Having spoken at both the American Federation for Medical Research (AFMR) as well as the Pediatric Academic Societies (PAS) meetings in 2023, I would wholeheartedly agree that artificial intelligence is very much needed as a resource for this domain, accompanied by a very high level of enthusiasm from the clinical researchers as a group.
The authors point out that clinical trials suffer from a myriad of issues, such as high financial cost, lengthy time frames, difficulties in recruitment and retention of participants, and disconnect between clinical research and medical care (the latter is also an issue with artificial intelligence research). The authors also discussed that an incredible 20% or more of clinical trial costs is related to the manual transfer and verification of data from the electronic record to a data capture system.
The ways in which AI can enhance the development, execution, and dissemination of clinical trials include: engagement and recruitment; consent; participant management; data capture and curation; outcomes and safety ascertainment; data analysis and reporting; and dissemination and implementation (all of these are found in a table in the editorial). The authors further suggested that domains of a regulatory framework for an AI-augmented clinical trial will need to include: reliability and validity; transparency; generalizability and bias; and privacy. The authors finally close this editorial by stating that the benefits from an AI-augmented approach for clinical research are innumerable, but the risk of exacerbating inequities as well as improvement of the black box of AI are important considerations.
To read the full article, click here.
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