Artificial intelligence (AI) is rapidly making its way into the exam room. From AI-powered medical scribes that document patient encounters to clinical decision-support tools, healthcare providers are increasingly using AI to improve efficiency, reduce administrative burdens and enhance patient care.
One of the most promising applications is ambient AI technology, which listens to conversations between providers and patients and generates clinical notes for inclusion in the electronic health record (EHR). For busy physicians, spending less time documenting encounters and more time interacting with patients is an appealing prospect.
However, AI also introduces legal and regulatory risks. Providers must consider how these technologies affect patient privacy, documentation accuracy, billing compliance and professional liability.
Patient Privacy and HIPAA Compliance
AI documentation tools may record, process and store conversations containing sensitive patient information. Providers must ensure that their use complies with the Health Insurance Portability and Accountability Act (HIPAA) and applicable state privacy laws.
Before implementing an AI tool, providers should understand how patient information is collected, stored and used. Does the vendor retain audio recordings? Is patient information used to train AI models? Who can access the information?
When an AI vendor handles protected health information on behalf of a covered provider, it will generally qualify as a business associate under HIPAA, requiring an appropriate Business Associate Agreement (BAA).[1]
Providers should not assume that an AI product marketed for healthcare automatically satisfies HIPAA requirements. Vendor due diligence and appropriate contractual safeguards remain essential.
Patient Consent and Transparency
The use of AI in the exam room also raises questions concerning patient consent.
Patients may be uncomfortable knowing their conversations are being recorded or analyzed by an AI system, particularly when discussing sensitive medical information.
Although HIPAA does not universally require separate patient authorization for AI-assisted documentation used for treatment, state recording laws and other requirements may apply.[2]
Even when express consent is not legally required, providers should consider informing patients when AI documentation technology is being used, explaining its purpose and offering an alternative when appropriate.
Transparency helps preserve the trust essential to the physician-patient relationship.
Documentation Accuracy and Billing Compliance
AI-generated documentation presents significant compliance risks.
Although AI tools can produce detailed clinical notes, they may misunderstand conversations, omit important information or generate statements that were never made. These inaccuracies, sometimes called AI "hallucinations," can become particularly problematic when incorporated into medical records.
For example, an AI-generated note might incorrectly indicate that an examination was performed, document findings that were never observed or include unsupported diagnoses.
Such errors can affect patient care and reimbursement. Medicare and commercial payors rely on medical records to determine whether services were medically necessary, properly coded and adequately documented.
Inaccurate AI-generated notes may expose providers to claim denials, overpayment demands and allegations of improper billing. These issues may not surface until months or years later during a payor or government audit.
The use of AI does not relieve providers of responsibility for ensuring that medical records accurately reflect the services furnished. Providers should carefully review and correct AI-generated documentation before finalizing records and submitting claims. Failure to do so may expose the provider to False Claims Act liability, which could result in treble damages and penalties for presenting false claims, using false records material to a claim and improperly avoiding an obligation.
Professional Liability and Clinical Decision-Making
AI is also increasingly used to assist with diagnoses, treatment recommendations and interpretation of clinical information.
While these tools may provide valuable assistance, providers should recognize their limitations. An inaccurate recommendation could contribute to a delayed diagnosis, inappropriate treatment or other adverse patient outcome.
Certain clinical decision-support software is subject to FDA oversight. Under the 21st Century Cures Act, one criterion for excluding qualifying clinical decision-support software from FDA medical-device regulation is that healthcare professionals can independently review the basis for its recommendations rather than relying primarily on the software. See 21 U.S.C. § 360j(o)(1)(E).
Providers should also recognize that AI systems may produce inaccurate or biased recommendations when their training data does not adequately represent the patients being treated.[3]
Ultimately, providers must exercise independent clinical judgment rather than relying unquestioningly on AI-generated recommendations and ensure that AI-generated recommendations are appropriate for the individual patient.
Implementing Appropriate Safeguards
Healthcare practices should establish policies governing AI use before incorporating these technologies into routine patient care. Important considerations include:
- Vendor evaluation: Review privacy protections, security practices, data retention policies, and contractual obligations.
- Patient consent: Determine applicable notification and consent requirements.
- Documentation review: Require providers to verify AI-generated notes before finalization.
- Billing compliance: Ensure documentation accurately supports billed services and medical necessity.
- Staff training: Educate physicians and staff regarding AI capabilities, limitations and appropriate use.
Practices should periodically reassess these policies as technologies and regulatory requirements evolve.
Conclusion
AI offers significant opportunities to reduce administrative burdens and improve healthcare delivery. However, these benefits must be balanced against the legal and compliance risks associated with its use.
By carefully evaluating AI vendors, establishing appropriate safeguards, and maintaining meaningful clinical oversight, healthcare providers can benefit from these technologies while reducing potential exposure to privacy violations, reimbursement disputes and professional liability.
While AI may transform how medical care is documented and delivered, it does not replace the provider's responsibility for patient care, accurate documentation and regulatory compliance.
Additional Resources for Healthcare Providers
Providers interested in learning more about the legal and regulatory considerations surrounding AI in clinical practice may find the following resources helpful:
- HHS Office for Civil Rights — HIPAA Business Associates: Guidance on HIPAA requirements applicable to third-party technology vendors handling patient information.
- FDA — Clinical Decision Support Software Guidance (2026): Guidance concerning FDA oversight of clinical decision-support technologies.
- ASTP/ONC — Decision Support Interventions and Predictive Models: Information concerning algorithm transparency and AI-enabled clinical decision-support tools.
[1] A business associate is a non-workforce person who, on a covered entity's behalf, creates, receives, maintains, or transmits PHI for a regulated function.
[2] For instance, certain states such as California, Florida, Illinois, Washington, and Pennsylvania require all-party consent for recording a conversation carrying a reasonable expectation of privacy. An exam room encounter likely meets this threshold. Because ambient AI scribes record every encounter by default, providers in all-party consent states should obtain and document patient consent to recording.
[3] Automation bias “describes the phenomenon whereby clinicians accept the guidance of an automated system and cease searching for confirmatory evidence, perhaps transferring responsibility for decision-making onto the machine—an effect reportedly strongest when a machine advises that a case is normal. Challen R, et al Artificial intelligence, bias and clinical safety. BMJ Qual Saf 2019;28:231–237, at 234; available at https://qualitysafety.bmj.com/content/qhc/28/3/231.full.pdf, BMJ Qual Saf 2019;28: p. 234.
