Your weekly update on how AI is changing our lives. This week we are focussing on medicinal matters.
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Your weekly update on how AI is changing our lives. Our experts keep it clear and simple, so you can stay ahead of the game. This week we are focussing on medicine. Don't forget to explore our Archive and Share & Subscribe with your friends!

Pathology

Zero-shot through the heart 💘 🎯

 

Big news has arrived from Europe, with a fascinating study by Swedish radiologists into the benefits of using AI assistants in cancer screening. The study compared the standard practice of 'double reading' - where two radiologists independently assess a patient, before comparing notes - with an AI-assisted screening process which only required a second human opinion in complex cases. They found that the AI-driven success rate was almost identical to that achieved with the traditional approach, but crucially, the work was carried out twice as fast, and without any increase in misdiagnosis. 

 

This is exciting news, but thanks to the pace that AI moves at, it’s also in danger of becoming old news. The study covers a period from April 2021 to July 2022, and a lot has happened since then; just this week Google announced a new AI model with a paper titled 'Towards Generalist Biomedical AI'. The new AI - Med-PaLM M - may sound similar to Med-PaLM, launched just a few months ago, but the newly acquired M in its name is a big deal. If you’ve been following chatbot trends closely you may already have guessed what it stands for: 'multimodal'.

Medbay

Even if you’re not familiar with the term, you’re likely to have encountered multimodality in chatbots already; it simply means the ability to handle many types of data in prompts, rather than just plain text. In Med-Palm M’s case this is an enormous upgrade because medical data comes in many forms; while the Swedish radiologists in the distant past of 2022 were achieving remarkable results with dedicated AI screening tools, Google’s new model is far more flexible, and equally comfortable reading reports, viewing X-rays, crunching genomic data and more. 

 

Here’s where it gets weird. This flexible approach enables what’s called 'zero-shot reasoning', where the AI is able to identify medical issues that are not part of its training. In Google’s study, the issues being spotted were signs of tuberculosis in chest X-rays, and Med-PaLM M almost matched the dedicated TB-spotter AI accuracy rate, despite having never been directly trained on the subject. This promises huge efficiency benefits across the entire healthcare sector, and should help to ease punishing workloads while improving patient outcomes. Google is at pains to stress that while certainly impressive, results will need to improve before the system is ready for general use, but the direction of travel doesn’t just seem to be 'Towards Generalist Biomedical AI'; we also seem to be accelerating.

 

Of course, it’s not just medicine where AI is revolutionizing science; don’t miss our in-depth look at the various ways that machine learning is boosting the speed of scientific discovery!

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