"Most things don't create a net aggregate benefit," said Daniel Nadler, founder of OpenEvidence, the Cambridge, Mass. AI startup that's training on Elsevier's massive collection of medical journal data. Usually someone wins and someone else loses, argued Nadler. But this is "a net aggregate benefit to every participant in the system."
Elsevier said it is working with 1,000 clinicians this month in an early access program with plans to release the tool more broadly next year.
It's not about typing into a generic AI system like ChatGPT or Bard, but rather combining the power of the models with domain-specific data. The result is that far fewer errors and "hallucinations" can occur than when querying a generic tool trained on the broader Internet.
[
add
]
[
|
|
...
]