Ambient AI in healthcare: Promise, risk, and responsible adoption

In a growing number of exam rooms across the nation, doctors aren’t the only ones listening. AI is, too.  

Ambient AI tools are allowing clinicians to step out from behind the computer monitor and focus on their patient while an AI agent listens, documents the visit, and, in many cases, organizes orders and referrals mentioned by the clinician. This technology is still in its early stages of adoption but has significant potential to reduce so-called clinician “pajama time,” improve coding accuracy, and enable a better patient experience.  

From an operational and financial perspective, there are clear benefits to ambient documentation. While traditional transcription helps clinicians generate real-time notes, ambient listening not only documents encounters but makes sense of them, extracting follow-up actions from the visit and presenting those actions for provider approval, eliminating hours of administrative work.  

However, healthcare leaders should carefully consider the legal and clinical risks of implementing this technology, including potential harms that can’t yet be imagined as AI continues to evolve. 

Data use without limits 

Unlike transcription, data from an encounter may be used repeatedly in future visits after the initial encounter—and not just for the patient whose care was being documented. Beyond clinical documentation, these systems also generate metadata on how patients and clinicians interact with them, such as how often commands are used to complete tasks. 

There’s virtually no limit to how often data from a patient encounter can be used, even implicitly, by AI, and use of the data may go beyond a patient’s health system. Models are always learning from the data they receive and generate, and sometimes that data is used to improve technology.  

This makes patient data a highly valuable commodity and raises critical questions: 

• Who owns this data, which is likely stored indefinitely?  
• Should patients be compensated for the value their data provides for technology firms and health systems?  
• If the company storing the data goes bankrupt or is sold, should patients have a say in how their data is sold or transferred?  

Deidentified data is frequently used in research and product development. However, the volume, persistence, and detail of ambient listening data introduces new questions around ongoing consent, reidentification risk, ownership, and commercialization for which there is little historical guidance.  

This isn’t a simple patient-provider privacy issue. Ambient listening data may implicate patients, clinicians, health systems, vendors, and others across the healthcare ecosystem, creating risks that range from reidentification and misuse to employment monitoring or commercial exploitation. The central challenge is that once encounter data is captured, its future uses—and future risks—may extend far beyond the clinical moment that produced it. 

Ambient AI may rely on biometric data from patients and providers 

To differentiate patients from providers, metadata from ambient AI may include biometric data, which is arguably even more sensitive than the protected health information health systems are charged with securing.  

This risk extends beyond patients. Ambient tools may repeatedly capture clinician and staff voices as they distinguish speakers, identify commands, or structure encounter notes. That makes consent, retention, vendor access, and data-use limits important workforce issues, not just patient privacy concerns. 

Legal risk related to biometric data isn’t theoretical; there have already been several high-profile cases. Illinois’ Biometric Information Privacy Act has become one of the country’s most heavily litigated privacy laws because it gives individuals a private right of action, allows statutory damages, and covers identifiers that can include voiceprints. For ambient listening tools, the key question is whether the technology is simply capturing audio for documentation or also creating, storing, or using voice-based identifiers to distinguish patients, clinicians, or staff. 

Autonomous decision-making could create risk 

Poor clinical documentation can lead to patient harm, regardless of whether it’s produced by a clinician or generated by AI. AI-driven omissions differ from human lapses because they may be harder to detect, easier to repeat at scale, and more difficult to assign responsibility for when something goes wrong.  

A clinician may omit an important detail because of time pressure, distraction, or clinical judgment—issues that can be remedied through training and workload adjustments. An AI tool may omit it because of transcription error, summarization logic, workflow configuration, prompt design, or some other reason that neither the clinician nor the health system may be able to fully understand or resolve. If the same tool is deployed across service lines, sites, or an entire health system, the same blind spot can be reproduced in thousands of notes before anyone recognizes the pattern. 

In its current form, ambient documentation can’t recognize non-verbal cues like a provider would. It can’t detect when a patient looks sad, anxious, or concerned. It can’t even tell when they nod their head “yes” or shake their head “no.” So while ambient documentation will free up providers from typing up notes on a keyboard, they may have to audibly let the device know about a patient’s body language, or at the least, thoroughly review the AI-generated record to confirm it accurately reflects the conversation.  

When poor provider documentation causes patient harm, clinicians are held accountable. But as ambient tools take over more of the note-generation process, clinicians will shift from documenting encounters to validating AI-generated records. This creates a new risk: under time pressure, clinicians may review AI-generated notes too quickly or assume that they’re accurate because they appear complete.  

As seen in other industries, AI outputs can invite over-reliance when users are expected to fact-check work that already looks finished. And if clinicians are no longer taking contemporaneous notes, they won’t be able to check the AI version of the encounter against a reliable independent record. 

How to balance innovation with risk  

Healthcare organizations that use ambient AI or are considering implementing it should take the following steps to mitigate risk and maintain patient trust.  

Establish an ambient AI governance process. Create a cross-functional review process involving clinical, legal, compliance, privacy, IT, clinical documentation improvement (CDI), and risk management leaders to evaluate how ambient tools are selected, implemented, monitored, and updated over time. 

Review data consent policies. Many patients who would give consent to make their providers’ lives easier may not want to let their health system or its vendors use data from their encounter in perpetuity. Review your patient consent forms to ensure that they’re compliant with wiretapping regulations and transparent about all possible uses of data collected by ambient AI. It’s also important to consider what the experience looks like for patients who decline to provide consent.   

Engage tech vendors. Health systems shouldn’t try to manage risk alone. Ask vendors about the data they collect, how they use and store it, and how long it’s retained. Both internally and with vendors, define how long audio, transcripts, metadata, biometric templates, and AI-generated notes are retained, who can access them, and when they’re deleted. 

Train clinicians for a new era of documentation. Tech and CDI leaders should train clinicians to prompt ambient AI tools effectively and scrutinize AI-generated notes for accurate, compliant documentation. Checklists—a commonly used tool in the OR—can guide clinicians through their review and promote accountability for patient safety.  

Create an escalation path for documentation concerns. Give clinicians a clear process for reporting recurring AI errors, unsafe outputs, or workflow issues so problems can be addressed before they spread across the organization. 

Audit documentation quality over time. Similar to CDI review, a cross-functional team of leaders should regularly review AI-generated notes for omissions, hallucinations, coding issues, bias, and variation across service lines. This is especially important because the same model behavior can be repeated across thousands of encounters. 

With ambient listening, clinical conversations are moving beyond documentation and becoming part of a persistent, AI-driven data ecosystem that requires thoughtful oversight. While it has the potential to make care more human by reducing administrative burden, that promise depends on how responsibly it’s governed. Healthcare leaders should capitalize on this opportunity for innovation but move carefully enough to manage the risks.