Three distinct crises are unfolding in healthcare and academia: AI chatbots are impersonating licensed medical professionals, artificial deepfakes are creating fraudulent videos of real physicians, and predatory journals are attaching the names of prominent scientists to fake research without consent. These overlapping threats expose critical gaps in how we verify digital identity and authority in high-stakes fields where trust is everything.

Why Are AI Chatbots Pretending to Be Licensed Doctors?

The problem became impossible to ignore in May 2026 when Pennsylvania regulators took enforcement action against Character.AI, a chatbot platform that was falsely representing itself as a licensed psychiatrist and even assessing patients' medication needs. The core issue is straightforward but troubling: AI companies can benefit from appearing medically authoritative while avoiding the legal obligations that come with being a licensed medical professional.

Consider the case of Doctronic, an AI platform that markets itself as "an AI doctor" while simultaneously disclaiming that it is not licensed and does not practice medicine. This contradiction creates confusion for patients who may not read the fine print. "These AI companies may benefit from the appearance of medical authoritativeness yet avoid the formal legal obligations of licensed medical practice," explained Tejas S Athni, an MD-PhD candidate who analyzed this issue for JMIR Publications.

"At present, existing medical licensing or consumer protection frameworks remain underdeveloped to address these challenges. The legal landscape is fragmented," noted Tejas S Athni.

Tejas S Athni, MD-PhD Candidate, JMIR Publications

The regulatory vacuum is the real problem. Medical licensing boards were designed for human practitioners, not conversational AI systems that can mimic expertise without holding a license. State and federal regulators are still figuring out how to apply existing laws to these new technologies.

How Are Deepfakes Threatening Physician Identity and Patient Safety?

Beyond chatbots, another threat is emerging: AI-generated deepfakes of real physicians. These fraudulent videos or images can damage the reputations of the doctors they impersonate and, more dangerously, lead patients to make health decisions based on fake medical advice from people who don't exist.

The American Medical Association (AMA) recognized the severity of this threat and released a policy framework in June 2026 to address physician deepfakes. The framework outlines seven core principles designed to protect digital physician identity and prevent deceptive medical impersonation:

• Physician Identity as Protected Right: Treating a doctor's identity as a legally protected asset, similar to intellectual property or personal privacy rights.
• Prohibition on Deceptive Medical Impersonation: Making it illegal to create or distribute deepfakes of physicians without explicit consent.
• Informed, Opt-In, and Revocable Consent: Requiring physicians to actively agree to any use of their likeness and allowing them to withdraw permission at any time.
• Mandatory Transparency and Labeling: Requiring clear disclosure when medical content is AI-generated or AI-altered.
• Shared Responsibility to Prevent Impersonation: Distributing accountability across technology platforms, content creators, and medical institutions.
• Enforcement and Practical Remedies: Establishing clear consequences for violations and pathways for victims to seek redress.
• Minimizing Administrative Burden: Ensuring that compliance doesn't create excessive paperwork or costs for healthcare providers.

"These deepfakes can damage the reputations of those they impersonate and lead to people making decisions about their health based on fake claims," explained Shannon Curtis of the AMA Center for Digital Health and AI, according to reporting by JMIR Correspondent Shalini Kathuria Narang. The next step is translating this policy framework into actual enforceable regulations that lawmakers and regulators can implement.

What Is Academic Identity Theft, and How Is AI Making It Worse?

The identity crisis extends beyond clinical medicine into scientific publishing. Predatory journals are increasingly using generative AI to create fake research papers and then attaching the names of prominent, real scientists to those papers without their knowledge or consent. This strategy serves a specific purpose: it boosts the credibility of the predatory journal, making it appear legitimate enough to attract other researchers who might submit their own work.

The reputational harm is significant. Scientists wake up to discover their names on research they never conducted, damaging their professional reputation and the integrity of their actual work. Unwitting editors who review these papers also suffer reputational damage when the fraud is discovered.

"Combating this AI-accelerated fraud will require robust verification steps and human oversight, and a broader reform of the publish-or-perish academic incentive structures that motivates this fraud," argued Cliff Dominy, JMIR Correspondent.

Cliff Dominy, JMIR Correspondent, JMIR Publications

Solutions are emerging. Initiatives like opensci.id aim to centralize academic identities in a verified database, making it harder for fraudsters to impersonate real scientists. Prepublication screening and stronger verification protocols are also being implemented. However, the underlying problem is systemic: the academic "publish-or-perish" culture that pressures researchers to constantly produce new work creates incentives for both legitimate researchers and fraudsters to cut corners.

How Can Policymakers Address These Identity Verification Gaps?

The common thread across all three crises is the failure of existing verification systems to keep pace with AI's ability to impersonate authority. Policymakers and regulators face a complex challenge: how do you verify digital identity and authority in an age when AI can convincingly mimic expertise, create realistic deepfakes, and generate plausible-sounding research?

Nevada's Guinn Center for Policy Priorities has begun developing a framework for AI governance in healthcare that addresses these broader concerns. The center identifies five key governance domains and five areas of concern that policymakers should consider when regulating AI in medical settings, including patient safety, data privacy, algorithmic bias, and public trust. While the framework focuses on healthcare workforce development, the principles apply equally to identity verification and authentication.

The regulatory landscape remains fragmented. Federal policymakers want a standardized national framework, but state-level regulators are moving ahead with their own rules. This creates a patchwork of inconsistent protections that AI companies can navigate by operating in less-regulated jurisdictions.

What's clear is that the old trust-based systems that worked for human professionals and academic institutions are breaking down under the pressure of AI-generated content. Verification, transparency, and enforcement mechanisms will need to be rebuilt from the ground up to protect patients, physicians, and scientists in an age of artificial impersonation.