2023 Danaher Summit Highlights: AI and Data

On data as a new kind of resource:

This is a common phrase: data is the new oil, AI is the new engine. I hate this phrase. Because as we know, especially in California, oil is grabby-grabby. Either I have that barrel of oil, or you have that barrel of oil. We can't both have the same barrel of oil. Data doesn't work that way. I might have a dataset and create something magical with that. You might have that same dataset and create something differently magical with that. So as another one of those TED talks says, data is the new soil. You plant your ideas in, data helps them grow.

Atul Butte, Priscilla Chan and Mark Zuckerberg Distinguished Professor, Director, Bakar Computational Health Sciences Institute

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On the challenges of working in a low data environment:

For us in pediatrics and in the emergency room we have the other side of the data problem where we don't have enough data. So I might have a one-year-old that comes in, for instance, and can't tell me that they have a severe headache. Can't tell their parent the same thing. How do I know what exactly is going on with this child? Or the EMS team calls in, an ambulance calls in, and they say, "I have an unresponsive ten-year-old and they're coming in. I have a heart rate of this, a blood pressure of this, we'll see you in two minutes." And so I'm working with no data or very limited data at that point, and this is where [bedside] diagnostics really come in.

Kemi Badaki-Makun, Professor of Pediatric Emergency Medicine, The Johns Hopkins Hospital

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On the need for actionable insights at the point of care:

From a practical perspective, doctors are under an enormous stress and we shouldn't lose sight of that, because they're asked to do more, absorb more, integrate more, with less time and more pressure to be productive... Whatever data comes down, I think we need to increasingly rely on the electronic medical record to distill and provide actionable information because in the middle of a 15-minute follow-up visit, the doctor cannot in any way integrate all the diagnostic information by his or herself, so we really are going to rely on informatics to help distill the critical information and make actionable decisions at the point of care.

Scott Friedman, Dean for Therapeutic Discovery and Chief of the Division of Liver Diseases, Icahn School of Medicine at Mount Sinai

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On AI's growing role as a partner for providers:

What AI needs to be is a really, really smart partner... An interesting study in Denmark of breast cancer patients throughout their history of disease found that at each diagnostic step, if you had two AI systems diagnose them, two pathologists or radiologists diagnose them depending on the task, or one and one - a computer and an expert - the computer and expert together were 20 points better than either two people or two computers.

Mara Aspinall, Partner, Illumina Ventures

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On the benefits of using existing data to train AI models:

A huge driver of innovation will be generating new insights from data that's just being ignored now... because physicians aren't trained to use it, they're unable to do it based on our limitations, whereas new deep learning models, foundation models, can really turn that into new insights. And then it's really solving a user's need and having products that really deliver higher quality at lower cost.

Andy Beck, CEO, PathAI

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On the need for better analytics:

I call this the data-insight gap. Basically data has been growing exponentially; in biology, insight grows linearly. Every day looks worse: percent data utilized is lower. And the answer is basically investing a huge amount in the analytics side, rather than data generation. That's the revolution we need to do to even justify the generation of that data.

Shai Shen-Orr, Associate Prof. at the Technion - Israel Institute of Technology, Founder & Chief Scientist, CytoReason

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On the importance of integrating new sources of data:

The biggest public health crisis we're going to be facing is neurologic diseases and neurodegenerative diseases. Early intervention and early detection is going to be key but the [current] tests are not great -- they're OK. The drugs are going to be really expensive. So that's when you think about wearables and other things: either cognitive tests online to detect early cognitive decline, movement disorders that can be detected with watches... these things exist. As a diagnostic laboratory, we have to think about the information they're creating and how we put it into an algorithm that would then trigger a more confirmatory lab test.

Bill Morice, President and CEO of Mayo Collaborative Services and Board Chair, American Clinical Laboratory Association

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