The Medical Futurist Interviews emtelligent's CEO, Dr. Tim O'Connell, on Demystifying AI's Reliability Concerns
"Providers, healthcare technology companies, payers, government agencies that provide healthcare services – they all need to have a chief AI officer, a person who understands the technology and workflows and can be responsible for implementation." - Dr. Tim O'Connell
Dr. Tim O'Connell is the founder and CEO of emtelligent, a Vancouver-based medical natural language processing technology solution, a practicing radiologist, and the vice-chair of clinical informatics at the University of British Columbia.
What are your thoughts on the recent widespread adoption of large language models in various sectors?
I'm really only qualified to talk about large language models (LLMs) in healthcare, but the adoption of this technology is exciting. LLMs are an amazing tool that can make language processing, language inference, and language understanding much more efficient and accurate. They offer an incremental performance improvement over previous types of natural language processing.
But a lot of people misunderstand large language models because of their own easy interactions with ChatGPT and Bard. They’re thinking, “Now I can converse with this amazing AI engine.” What they tend to overlook is that these language models still have reliability problems in terms of accuracy and “hallucinations” (a phenomenon in which an AI model, based on the training data it has been fed, generates outputs that aren’t possible in the real world). For safe use in healthcare, LLMs must be trained on extremely large sets of annotated medical data to improve accuracy.
Still, I’m bullish on the future of LLMs. They are a super exciting technology that's moving the needle on how a computer understands language. They’re going to open up new markets and new possibilities. They will help people do their jobs better and more safely in healthcare. And they’ll help relieve physician burnout by taking over some of the tasks that consume so much clinician time.
Read the entire interview here.