Category: Artificial Intelligence

The House Bipartisan Task Force on Artificial Intelligence closed out the year with a bang, launching 273-pages of AI policy fireworks.

The report includes recommendations to “advance America’s leadership in AI innovation” across multiple industries, and the healthcare section definitely packed a punch.

The task force started by highlighting AI’s potential across a long list of use cases, which could have been the tracklist for healthcare’s greatest hits of 2024:

• Drug Development – 300+ drug applications contained AI components this year.
• Ambient AI – Burnout is bad. Patient time is good.
• Diagnostics – AI can help cut down on $100B in annual costs tied to diagnostic errors.
• Population Health – Population-level data can feed models to improve various programs.

While many expect the Trump administration’s “AI Czar” David Sacks to take a less-is-more approach to AI regulation, the task force urged Congress to consider guardrails in key areas:

• Data Availability, Utility, and Quality
• Privacy and Cybersecurity
• Interoperability
• Transparency
• Liability

Several recommendations were offered to ensure these guardrails are effective, although the task force didn’t go as far as to prescribe specific regulations. 

• The report suggested that Congress establish clear liability standards given that they can affect clinical-decision making (the risk of penalties may change whether a provider relies on their judgment or defers to an algorithm).

• Another common theme was to maintain robust support for healthcare research related to AI, which included more NIH funding since it’s “critical to maintaining U.S. leadership.” 

The capstone recommendation – which was naturally well-received by the industry – was to support appropriate AI payment mechanisms without stifling innovation.

• CMS calculates reimbursements by accounting for physician time, acuity of care, and practice expenses, yet fails to adequately reimburse AI for impacting those metrics.

• The task force said there won’t be a “one size fits all” policy, so appropriate payment mechanisms should recognize AI’s impact across multiple technologies and settings (Ex. many AI use cases may fit into existing benefit categories or facility fees).

The Takeaway

AI arrived faster than policy makers could keep up, and it’ll be up to the incoming White House to get AI past its Wild West regulatory era without hobbling the pioneers driving the progress. One way or another, that’s a sign that AI is starting a new chapter, and we’re excited to see where the story goes in 2025.

Bain & Company’s US Frontline of Consumer Healthcare Survey turned up the surprising result that patients are more comfortable with generative AI “analyzing their radiology scan and making a diagnosis than answering the phone at their doctor’s office.”

That’s quite the headline, but the authors were quick to point out that it’s probably less of a measure of confidence in GenAI’s medical expertise than a sign that patients aren’t yet comfortable interacting with the technology directly.

Here’s the breakdown of patient comfort with different GenAI use cases:

While it does appear that patients are more prepared to have GenAI supporting their doctor than engaging with it themselves, it’s just as notable that less than half reported feeling comfortable with even a single GenAI application in healthcare.

• No “comfortable” response was above 37%, and after adding in the “neutral” votes, there was still only one application that broke 50%: note taking during appointments.

• The fact that only 19% felt comfortable with GenAI answering calls for providers or payors could also just be a sign that patients would far rather talk to a human in either situation, regardless of the tech’s capabilities.

The next chart looks at GenAI perceptions among healthcare workers: 

Physicians and administrators are feeling a similar mix of excitement and apprehension, sharing a generally positive view of GenAI’s potential to alleviate admin burdens and clinician workloads, as well as a concern that it could undermine the patient-provider relationship.

• Worries over new technology threatening the relationship of patients and providers aren’t new, and we just witnessed them play out at an accelerated pace with telehealth.

• Despite initial fears, the value of the relationship prevailed, which Bain backed up with the fact that 61% of patients who use telehealth only do so with their own provider.

Whether you’re measuring by patient or provider comfort, GenAI’s progress will be closely tied to trust in the technology on an application-by-application basis. Trust takes time to build and first impressions are key, so this survey underscores the importance of nailing the user experience early on.

The Takeaway
The story of generative AI in healthcare is just getting started, and as we saw with telehealth, the first few pages could take some serious willpower to get through. New technologies mean new workflows, revenue models, and countless other barriers to overcome, but trust will only keep building every step of the way. Plus, the next chapter looks pretty dang good.

AI is getting pretty darn good at patient diagnosis challenges… but don’t bother asking it to show its work.

A new study in npj Digital Medicine pitted GPT-4V against human physicians on 207 image challenges designed to test the reader’s ability to diagnose a patient based on a series of pictures and some basic clinical background info.

• Researchers at the NIH and Weill Cornell Medicine then asked GPT-4V to provide step-by-step reasoning for how it chose the answer.

• Nine physicians then tackled the same questions in both a closed-book (no outside help) and open-book format (could use outside materials and online resources).

How’d they stack up?

• GPT-4V and the physicians both scored high marks for accurate diagnoses (81.6% vs. 77.8%), with a statistically insignificant difference in performance. 

• GPT-4V bested the physicians on the closed-book test, selecting more correct diagnoses.

• Physicians bounced back to beat GPT-4V on the open-book test, particularly on the most difficult questions.

• GPT-4V also performed well in cases where physicians answered incorrectly, maintaining over 78% accuracy.

Good job AI, but there’s a catch. The rationales that GPT-4V provided were riddled with mistakes – even if the final answer was correct – with error rates as high as 27% for image comprehension.

The Takeaway

There could easily come a day when clinical AI surpasses human physicians on the diagnosis front, but that day isn’t here quite yet. Real care delivery also doesn’t bless physicians with a set of multiple choice options, and hallucinating the rationale behind diagnoses doesn’t cut it with actual patients.

When it rains it pours for AI research, and a trio of studies published just last week suggest that many new generative AI tools might not be ready for prime time with patients.

The research that grabbed the most headlines came out of UCSD, finding that GenAI-drafted replies to patient messages led to more compassionate responses, but didn’t cut down on overall messaging time.

• Although GenAI reduced the time physicians spent writing replies by 6%, that was more than offset by a 22% increase in read time, while also increasing average reply lengths by 18%.

• Some of the physicians were also put off by the “overly nice” tone of the GenAI message drafts, and recommended that future research look into “how much empathy is too much empathy” from the patient perspective.

Another study in Lancet Digital Health showed that GPT-4 can effectively generate replies to health questions from cancer patients… as well as replies that might kill them.

• Mass General Brigham researchers had six radiation oncologists review GPT-4’s responses to simulated questions from cancer patients for 100 scenarios, finding that 58% of its replies were acceptable to send to patients without any editing, 7% could lead to severe harm, and one was potentially lethal.

• The verdict? Generative AI has the potential to reduce workloads, but it’s still essential to “keep doctors in the loop.”

A team at Mount Sinai took a different path to a similar conclusion, finding that four popular GenAI models have a long way to go until they’re better than humans at matching medical issues to the correct diagnostic codes.

• After having GPT-3.5, GPT-4, Gemini Pro, and Llama2-70b analyze and code 27,000 unique diagnoses, GPT-4 came out on top in terms of exact matches, achieving an uninspiring accuracy of 49.8%.

The Takeaway

While it isn’t exactly earth-shattering news that GenAI still has room to improve, the underlying theme with each of these studies is more that its impact is far from black and white. GenAI is rarely completely right or completely wrong, and although there’s no doubt we’ll get to the point where it’s working its magic without as many tradeoffs, this research confirms that we’re definitely not there yet.