Letting doctors
be doctors
Current ambient AI assistants, which gained mainstream traction in 2023, are already able to record, structure, and summarize patient encounters in real time. This liberates clinicians from the time-consuming exercise of writing notes, allowing them to fully engage with their patients. “For complex patients, it could take me up to 45 minutes to complete the documentation. Nabla makes that task infinitely better and allows me to give each patient my full, undivided attention. At the end of the visit, I click, and Nabla produces a thoughtfully crafted, concise record of what happened,” says Lee, who puts the accuracy of Nabla’s system in the “high 90s” in terms of percentage, with the clinician always responsible for reviewing and signing off on the final record.
“For complex patients, it could take me up to 45 minutes to complete the documentation. Nabla makes that task infinitely better and allows me to give each patient my full, undivided attention. At the end of the visit, I click, and Nabla produces a thoughtfully crafted, concise record of what happened.”
Dr. Ed Lee, Chief Medical Officer, Nabla
This kind of uninterrupted patient engagement can lead to better eye contact and a higher quality interaction. For instance, clinicians tend to verbalize their thought process more when there is alternative notetaking during a patient evaluation. “We originally thought that patients would be worried about an AI device listening, but actually they are very excited,” says Alexandre LeBrun, co-founder and chief executive officer of Nabla. “They get the full attention of their physician during the visit, and they love when they hear technical language as they sense they get better care.”
According to LeBrun, Nabla’s system can further support clinicians by automating pre-charting, reviewing and organizing a patient’s information in their EHR before an appointment, and coding medical data for use in areas like billing. Nabla has also expanded its platform with a built-in dictation capability, bringing clinicians closer to a unified experience. These kinds of AI assistant tasks can help to streamline and enhance clinical workflows and contribute to a reduction in institutional administrative costs.
The promise of
agentic AI
Agentic AI, which companies like Nabla are currently working to integrate into their systems, promises to take the success of existing AI assistants a step further. LeBrun is looking to a future in which clinicians interact with an agentic platform that links to all the tools they already use and simplifies multi-step interactions, like reading patient data, acting within the EHR, and adapting to workflows in real time.
“Rather than forcing doctors and nurses to click through a dozen separate systems, our platform will provide specialized, customizable, and composable agents that turn disconnected tools into a single, continuous workflow,” LeBrun says.
“Imagine a cardiologist getting ready for their morning clinic. After a few voice commands to instruct the system, one agent pulls the latest vitals, lab results, and imaging reports from the EHR, another generates a clear patient summary, and a third flags a missed follow-up echocardiogram. All before the patient even walks into the room,” LeBrun explains.
“Rather than forcing doctors and nurses to click through a dozen separate systems, our platform will provide specialized, customizable, and composable AI agents that turn disconnected tools into a single, continuous workflow.”
Alexandre LeBrun, Co-founder and Chief Executive Officer, Nabla
Lee says that agentic AI’s near-term scope includes standardized and protocolized non-clinical tasks, but he sees promise in areas like treatment options and other types of clinical decision support, where AI can safely operate with clinicians always “in the loop.”
To get to this point, education is essential, says Lee. “The beauty of medicine is that it’s a lifelong learning process. It’s not just learning about the science behind medications, diagnoses, and treatments; it’s about adapting to the use of new tools that will ultimately improve the care of the patients you treat,” he explains.
“We need to start with the basics of AI, making sure everyone understands what it is and how it works. Not how the programming takes place but more around what it can do, what it can’t do, the risks and pitfalls, and then really understanding where it fits best in the care of patients,” says Lee.
Leadership must look ahead strategically and ensure the entire organization is moving forward with its use and understanding of AI, he adds. “Part of that journey is involving frontline users to be part of the process, co-designing whenever possible and conducting pilots of new solutions so the organization can learn,” Lee says. Additionally, “a culture of inclusivity, authenticity, and transparency needs to be in place so you can be in the best position to be successful with transformative efforts such as incorporating and integrating agentic AI into the ecosystem,” he says.
“Part of that journey is involving frontline users to be part of the process, co-designing whenever possible and conducting pilots of new solutions so the organization can learn.”
Dr. Ed Lee, Chief Medical Officer, Nabla
Safely integrating
into workflows
Applying AI to high-stakes sectors like health care requires a careful balance between productivity on the one hand, and accuracy on the other. “Trust is everything in medicine,” says LeBrun. “Earning that trust means giving clinicians confidence through accuracy, transparency, and respect for their expertise.” Nabla uses techniques like adversarial training models to check outputs, and it defaults to conservative responses. “We optimize precision. If we have a slight doubt, we prefer to remove something from the output by default,” says LeBrun
“Trust is everything in medicine. Earning that trust means giving clinicians confidence through accuracy, transparency, and respect for their expertise.”
Alexandre LeBrun, Co-founder and Chief Executive Officer, Nabla
New tools must also interweave with existing workflows and platforms to avoid adding more complexity for clinicians. “Any product can look great, but if it doesn’t fit well into your existing workflows, it’s almost useless,” says LeBrun.
In sectors like customer service, it is straightforward to build a new interface or platform, but that approach isn’t feasible—or desirable—in health care. “It’s a complex network of dependencies with so many workflows and processes,” says LeBrun. “Everybody would like to get rid of these things, but it’s not possible because you would need to change everything at once.” Agentic AI approaches offer great promise to sectors like health care because they can “improve the process without getting rid of the legacy infrastructure,“ LeBrun explains.
By simplifying complex systems, automating routine tasks, and continuing to take on more of the time-consuming burden of administrative work, agentic AI holds great promise in further augmenting ambient AI assistants. Ultimately, the technology’s potential is not in making medical decisions or replacing clinicians, but in supporting health care workers to dedicate more of their time and attention to their main priority: their patients. “AI should focus on supporting decisions and automating everything downstream,” says LeBrun. “The first role of AI is to get physicians back to the state where they make medical decisions.”
Discover more insights from Nabla here.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
