Category: Artificial Intelligence

AI Executive Order, the Full Breakdown

Posted on by Jason Barry

The White House’s long-awaited executive order on “Safe, Secure, and Trustworthy” artificial intelligence is finally here, and it left little room to miss its underlying message: the laissez-faire era of AI regulation is over.

Among the 100+ pages of actions guiding the direction of responsible AI development, President Biden laid out several initiatives poised to make an immediate impact within healthcare, including…

  • Calling on HHS to create an AI task force within six months to assess new models before they go to market and oversee their performance once they do
  • Requiring that task force to build a regulatory structure that can “maintain appropriate levels of quality” in AI used for care delivery, research, and drug development
  • That structure will require healthcare AI developers to share their safety testing outcomes with the government
  • Balancing the added regulation by ramping up grantmaking for AI development in areas such as personalized immune-response treatments, burnout, and improving data quality
  • Standing up AI.gov to serve as the go-to resource for federal AI standards and hiring, a decent signal that there’ll be actual follow-through to cultivate public sector AI talent

The FDA has already approved upwards of 520 AI algorithms, and has done well with predictive models that take in data and propose probable outcomes. 

  • However, generative AI products that respond to human queries require “a vastly different paradigm” to regulate, and FDA Digital Health Director Troy Tazbaz believes any new structure will involve ongoing audits to ensure continuous safety.

There’s already been tons of great post-game analysis on these developments, with the general consensus looking like a cautious optimism. 

  • While some appreciate the order’s whole-of-government approach to AI, others worry that “excessive preemptive regulation” could slow AI’s progress and delay its benefits.
  • Others are skeptical that the directives will be carried out at all, given the difficulty of hiring enough AI experts in government and passing the needed legislation.

The Takeaway

President Biden’s executive order aims to thread the needle between providing protection and encouraging innovation, but time will tell whether it’ll deliver on some much-needed guardrails. Although AI is a lightning-quick industry that doesn’t exactly lend itself to the type of centralized long-term planning envisioned in the executive order, more structure should be an improvement over regulatory uncertainty.

Abridge Lands $30M As AI Race Heats Up

Posted on by Jason Barry

Momentum makes magic, and few startups have more of it than AI medical scribe Abridge after landing $30M in Series B funding from Spark Capital and high-profile strategics like CVS Health, Kaiser Permanente, and Mayo Clinic.

Abridge’s generative AI platform converts patient-provider conversations into structured note drafts in real-time, slashing hours from administrative burdens by generating summaries that rarely require further input (clinicians edit less than 9%).

The Series B is one of this year’s largest raises for pure play healthcare AI, an area that now accounts for about a quarter of all capital flowing into health IT.

One of the reasons why investors are taking such a keen interest in Abridge is its partnership hot streak, which includes Epic bringing them on as the first startup in its new Partners and Pals program – a move that will make Abridge available directly within Epic’s EHR.

  • It also probably doesn’t hurt that Abridge isn’t shy about sharing its performance data and machine learning research, giving it one of the deepest publication libraries of any company we’ve ever covered.
  • On top of that, Abridge has been racking up a lengthy list of deployments at health systems such as UPMC, Emory Healthcare, and University of Kansas Health System.

The competition is fierce in the AI scribe arena, which is packed with hungry startups like Suki and Nabla, as well as a thousand-pound gorilla named Nuance Communications. 

  • Half a million doctors use Nuance’s DAX dictation software, with “thousands” more already up-and-running on its new fully-automated DAX Copilot.

Some key differentiators give Abridge and its user base of 5,000 clinicians a solid shot at closing the distance, including “linkages” that map everything in the note to its source in both the transcript and audio (Nuance provides the transcript but not the recording). 

  • Abridge also developed its own ASR stack (automatic speech recognition), enabling it to do things like account for new medication names and excel at multilingual documentation, meaning it can generate an English note from a Spanish conversation.

The Takeaway

Abridge is emerging as a standout in the clinical documentation race, with DNA that’s as healthcare-native as it is AI-native. The executive team is lined with practicing physicians and machine learning experts, giving Abridge an advantageous understanding of not only the technology, but also the hurdles it will take for that technology to take hold in healthcare.

Study: AI is in the Eye of the Beholder

Posted on by Jason Barry

At a time when new healthcare AI solutions are getting unveiled every week, a study in Nature Machine Intelligence found that the way people are introduced to these models can have a major effect on their perceived effectiveness.

Researchers from MIT and ASU had 310 participants interact with a conversational AI mental health companion for 30 minutes before reviewing their experience and determining whether they would recommend it to a friend.

Participants were divided into three groups, which were each given a different priming statement about the AI’s motives:

  • No motives: A neutral view of the AI as a tool
  • Caring motives: A positive view where the AI cares about the user’s well-being
  • Manipulative motives: A negative view where the AI has malicious intentions

The results revealed that priming statements certainly influence user perceptions, and the majority of participants in all three groups reported experiences in line with expectations.

  • 88% of the “caring” group and 79% of the “no motive” group believed the AI was empathetic or neutral – despite the fact that they were engaging with identical agents.
  • Only 44% of the “manipulative” group agreed with the primer. As the authors put it, “If you tell someone to be suspicious of something, then they might just be more suspicious in general.”
  • As might be expected, participants who believed the model was caring also gave it higher effectiveness scores and were more likely to recommend it to a friend. That’s obviously relevant for those developing similar mental health chatbots, but a key insight for presenting any AI agent to new users.

An interesting feedback loop was also found between the priming and the conversation’s tone. People who believed the AI was caring tended to interact with it in a more positive way, making the agent’s responses drift positively over time. The opposite was true for those who believed it was manipulative. 

The Takeaway

The placebo effect is a well documented cornerstone of medical literature, but this might be the first study to bridge the phenomenon from sugar pill to AI chatbot. Although AI is often thought of as primarily an engineering problem, this research does a great job highlighting how human factors and the power of belief play a huge role in the perceived effectiveness of the technology.

Bain & Co: Getting the Most Out of Generative AI

Posted on by Jason Barry

Bain & Company is back at it again with more generative AI research, this time offering a series of ways for providers to get the most out of the tech without falling into potholes of hype.

The in-depth report gives a comprehensive overview of the current generative AI landscape, and delivers solid insight into the priorities of health system executives (N=94): 

  • Top use case priorities (next 12 months): charge capture & reconciliation (39), structuring & analysis of patient data (37), workflow optimization (36). [Chart 1]
  • Top use case priorities (2-5 years): predictive analytics & risk stratification (44), clinical decision support (41), diagnostics & treatment recommendations (37). [Chart 2]
  • Biggest barriers to implementation: resource constraints (46), lack of technical expertise (46), regulatory & legal considerations (33). [Chart 3]

Start small to go big. Although the survey itself included some valuable stats, the spotlight was stolen by Bain’s particularly pragmatic framework for guiding new implementations. 

  • Pilot low-risk applications with a narrow focus. Bain found that the systems already seeing the most success with generative AI are testing solutions in low-risk use cases where they already have the right data and can create tight guardrails (chatbot support, scheduling, rev cycle).
  • Decide to acquire, partner, or build. Bain recommends that CEOs think about different use cases based on availability of third-party tech and importance of the initiative.
  • Funnel experience into bigger initiatives. As generative AI starts to mature, organizations that gain experience and strategy alignment today will be best positioned for the more transformative use cases once they become clear.
  • Generative AI isn’t a strategy unto itself. Bain found that the trait separating top CEOs is their discipline, ensuring that every generative AI initiative reinforces their overarching goals as opposed to implementing shiny bells and whistles.

The Takeaway

It’s easy to get caught up in the generative AI hype cycle, so it was refreshing to see Bain recommend the one-foot-in-front-of-the-other approach to new implementations. Nearly every hospital boardroom is debating a massive list of potential AI investments, and although the home run use cases will be here soon, the consensus strategy for getting on base seems to be making low-risk plays with an immediate impact.

Hippocratic Emerges to Bring LLMs to Healthcare

Posted on by Jason Barry

Although we touched on Hippocratic AI’s emergence from stealth last week, the startup struck all the right chords with its talk of generative AI and large language models so we’re doing a deeper dive to unpack the hype. 

Hippocratic AI debuted with $50M from a massive seed round co-led by the VC power duo of General Catalyst and a16z, giving the company a “triple-digit millions” valuation right out of the gate.

  • The company’s mission is to transform healthcare through the power of “safety-focused” generative AI, but potential use cases are still in the works, with early ideas revolving around consumer-facing tasks like diet planning and medication reminders.
  • The founders even told STAT that their goals for the funding are only exploratory: developing a LLM that’s fine-tuned for healthcare, heavily testing it against knowledge benchmarks, then measuring its bedside manner. 

If including Hippocratic in the name wasn’t enough of a hint, pressure-testing the model’s accuracy is in the company’s DNA, and it isn’t planning on rushing into clinical care.

  • Accuracy is ensured through reinforcement learning from human feedback (RLHF) performed by medical professionals – a strategy that’s apparently outperforming GPT-4 on 105 of 114 healthcare certifications. [Comparison Chart]
  • As for bedside manner, Hippocratic is planning on “detecting tone” and “communicating empathy” better than rival models, and developed its own benchmark for behaviors such as “taking a personal interest in a patient’s life.”

The Takeaway

Healthcare hasn’t always been kind to AI-first businesses, with IBM having to let go of its Watson Health division and Babylon recently meeting the end of its road as a publicly traded company. That said, both of those examples paddled too early to catch the current generative AI wave with its mile-long barrel of new tech and excitement. It’s too early to tell whether Hippocratic will buck the trend, but if there was ever a moment to try it – this is it.

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