Google believes there’s an opportunity to offload more healthcare tasks to productive AI models — or at least, an opportunity to recruit those models to aid of health workers in the completion theirs duties.
Today, the company announced MedLM, a family of models optimized for the medical industries. Based on Med-PaLM 2, a model developed by Google that performs at an “expert level” on dozens of medical exam questions, MedLM is available to Google Cloud customers in the US (it’s in preview in some other markets) who have whitelist through Vertex AI, Google’s fully managed AI developer platform.
There are two MedLM models currently available: a larger model designed for what Google describes as “complex tasks” and a smaller, granular model best for “scaling across tasks.”
“Through piloting our tools with different organizations, we’ve learned that the most effective model for a given task varies by use case,” says a suspension written by Yossi Matias, Google’s VP of Engineering and Research, provided to TechCrunch ahead of today’s announcement. “For example, conversation summarization may be better handled by one model, and drug search may be better handled by another.”
Google says an early user of MedLM, for-profit facility operator HCA Healthcare, is piloting the models with doctors to help write patient notes in hospital emergency department settings. Another reviewer, BenchSci, has incorporated MedLM into its novel “evidence engine” for identifying, classifying, and ranking biomarkers.
“We work closely with practitioners, researchers, health and life science organizations, and people on the front lines of healthcare every day,” writes Matias.
Google – along with arch-rivals Microsoft and Amazon – are desperately racing to drive a market for AI in healthcare that could be worthwhile tens of billions of dollars to 2032. Recently, Amazon launched AWS HealthScribe, which uses genetic artificial intelligence to transcribe, summarize and analyze notes from patient-doctor conversations. Microsoft is piloting various AI healthcare products, including medical “assistant” applications supported by large language models;
But there is reason to be wary of such technology. Artificial intelligence in healthcare has historically been met with mixed success.
Babylon Health, an artificial intelligence startup backed by the UK’s National Health Service, has come under repeated scrutiny for claiming its disease-diagnosing technology can outperform doctors. And IBM was forced to sell its artificial intelligence-focused Watson Health division at a loss after technical problems led to customer partnerships deteriorating.
One could argue that production models like those of Google’s MedLM family are much more sophisticated than what came before them. However, research has shown that generative models are not particularly accurate when it comes to answering healthcare-related questions, even fairly basic ones.
A study authored by a group of ophthalmologists asked ChatGPT and Google’s Bard chatbot questions about eye conditions and diseases and found that the majority of responses from all three tools were wildly off the mark. ChatGPT creates cancer treatment plans full of potentially fatal errors. And models like ChatGPT and Bard spew racist, debunked medical ideas in response to questions about kidney function, lung capacity, and skin.
In October, the World Health Organization (WHO) warned of the risks of using genetic artificial intelligence in healthcare, pointing to the potential for the models to generate harmful wrong answers, spread health misinformation and reveal health data or other sensitive information. . (Because models occasionally memorize training data and return portions of that data at the correct prompt, it is possible that models trained on medical records could accidentally leakage these files.)
“While WHO is enthusiastic about the appropriate use of technologies, incl [generative AI]to support healthcare professionals, patients, researchers and scientists, there is concern that the attention that would normally be exercised for any new technology is not consistently exercised with [generative AI]”, the WHO said in a statement. “Rapid adoption of untested systems could lead to errors by healthcare workers, harm patients, erode confidence in AI and thereby undermine or delay potential long-term benefits and uses of such technologies throughout the world”.
Google has repeatedly claimed to be extremely cautious about releasing AI healthcare tools, and it’s not changing its pace today.
“[W]we are focused on empowering professionals to use this technology safely and responsibly,” continued Matias. “And we’re committed not only to helping others advance health care, but to making sure those benefits are available to everyone.”
