Artificial intelligence can transform medical imaging - so why not see it more?

Artificial intelligence can transform medical imaging – so why not see it more?

Editor’s note: This story is the third part of… our chain On emerging AI companies and their impact on multiple sectors. in part OneIn this article, we analyze venture capital investment in artificial intelligence over the past decade. The second part Looks at the billions of dollars being spent on AI-enhanced cybersecurity. — Kristin Kilpatrick, Special Projects Editor

There are too few doctors in the United States, and too many patients who need them.

Amid physician fatigue and long waiting lists to see specialists, a specialized technology that has seen slow adoption rates is suddenly in demand: medical imaging that uses artificial intelligence to aid diagnosis. This technology can help pre-screen patients or work alongside physicians to scan images and help find problems that might otherwise go unnoticed by the tired, weary human eye.

Funding for startups with this technology jumped from $348 million to more than $1 billion between 2020 and 2021, according to Crunchbase data. Although that figure is down to $883 million so far in 2022, it’s still the second-largest year for AI in diagnostics funding to date.

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“[Adoption] It was very slow until COVID,” He said Sarah Choia biotechnology investor in VC wing. “Now, I think there is a renewed focus on anything that fixes physician or clinician burnout.”

However, despite its inherent advantages, doctors are still concerned about this new technology.

“It really requires a very nuanced understanding of: How is this going to fit into a physician’s workflow?” He said Jacob Ephrona healthcare-focused investor in Redpoint Ventures. “How do you fit into the incentives of the different people in the system?”

The Doctor’s Answer to Burnout

The shortage of doctors in America has effectively transformed every clinic, doctor’s office, and care organization into needs-based systems where only the most urgent patients can see a doctor at the right time.

He said “Humans miss out on a lot of diseases because there’s an ingrained mentality where they think, ‘Can I treat this patient tomorrow?'” ” Kaushal SolankiCEO and founder of a medical imaging AI startup Enoch. “This is not my favorite limit.”

Doctors deal with large groups of patients every day, unable to spend enough time with each patient to treat them better. This leads to burnout, which in turn translates into lower quality care. More importantly, patients who can’t see a doctor often are treated for problems that could have been avoided if caught early.

This is where companies like Eyenuk come in. The 12-year-old California-based company’s platform can independently diagnose diabetic retinopathy, a disease that grows quietly behind the eye and can worsen without immediate medical intervention. It was granted Food and Drug Administration in 2020 and has processed about 2 million images so far. Eyenuk raised $26 million in Series A funding in October led by AXA Investment Managersaccording to Crunchbase data.

The Eyenuk platform screens patients and allows ophthalmologists to prioritize who they see based on need. But the goal is to one day put the device in hospitals and primary care offices so doctors can check patients’ eyes instead of referring them to a hard-to-find specialist.

“This could be run by anyone with a high school diploma and produce a workable report that can outline the next steps for a patient, whether they are referred to an ophthalmologist or eye care professional, or show up next year for a repeat examination,” Solanki said.

This type of medical imaging technology is used in a large number of other sectors as well. Pearl, a California-based dental startup that has raised $11 million, has a platform called Second Opinion (can you guess why?) that scans dental imaging to demonstrate a variety of dental ailments to doctors. Based in Israel Eduk Provides radiology-focused AI tools to customers who scan radiographs to look for potential problems. The company has raised more than $237 million.

Adoption is still lagging behind

While adoption rates have skyrocketed during the pandemic, medical imaging AI is not yet as widespread as its owners had hoped.

“It’s actually not a problem that the technology is not advanced enough,” Choi said. “It’s an adoption issue, and really validating use cases to convince providers that there is commercial value as well as clinical value to these solutions.”

Acceptance by providers is critical to almost any type of healthcare offering, but sticking to a schedule full of patients makes it difficult to learn and adopt new technology that may get in the way of work, especially if they don’t think it will add much value.

There is good reason for this suspicion. The American College of Radiology It found that most AI platforms are not independently validated, which calls into question the accuracy of these platforms. The Food and Drug Administration It doesn’t have a consistent qualification as to how large or diverse the training data set is.

We need these models to function transparently and be interpretable. And that’s the difference doctors are looking for — because the doctor deserves to know how these machine learning models read their patients,” said William Padula, assistant professor of pharmacoeconomics and health at the University of Michigan. University of Southern California. “The fear here is that while the programmer knows what they did to create the model, it is unclear how exactly he looks at the patient. “

But the promise of artificial intelligence cannot be underestimated. In a post-pandemic health system, accessible diagnostic resources will be important. Public health officials are paying For more accessible or at home diagnostic testing for all types of ailments. and diagnoses 70% of all healthcare-related decision-making.

“We think the technology should be good enough to work on its own. That actually creates value for the system,” Solanki said. “Now there’s one less thing for professionals to worry about, which is routine inspection.”

Check back for Part 4 of our series, which highlights some of the creative ways startups are applying AI in their sectors.

Clarification: Dom Guzman

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