Why healthcare staff are struggling to spot the fakes

Deepfakes use advanced artificial intelligence to create audio and video content that looks and sounds completely real, even when it isn’t. That means fake patients, colleagues, or even supervisors can be convincingly imitated. The technology behind deepfakes is evolving so quickly that the tools designed to detect them often can’t keep up, especially in fast-paced clinical environments. 

As one study ‘Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students’ explains, “DeepFakes are synthetic performances created by AI, using neural networks to exchange faces in images and modify voices,” allowing users to replicate a person’s appearance or speech so convincingly that “they appear authentic to the eyes of a human being.” This makes it increasingly difficult for even trained professionals to know what is real and what is fabricated. Researchers further note that “various experiences speak of the difficult task of distinguishing between authentic images or videos and DeepFakes, which is a major technological and human challenge.”

Modern deepfake algorithms have become so refined that the telltale signs are now barely noticeable or entirely absent. Healthcare workers attention is focused on patient care, not digital forensics, and they rarely receive specialized training to spot synthetic media. 

The problem of fakes in healthcare

Modern deepfakes are powered by advanced generative models like generative adversarial networks (GANs) and diffusion models (DMs). As a Journal of Imaging study explains, “The rapid advancement of artificial intelligence (AI) has given rise to a new wave of synthetic media, widely known as deepfakes. While offering unprecedented creative possibilities, these technologies have also raised substantial ethical and security concerns, posing risks in domains such as entertainment, politics, and cybersecurity.” 

The report adds that deepfakes are “often indistinguishable from authentic media, which has led to their misuse in spreading misinformation, impersonation, and other malicious activities.” Deepfakes have already been deployed to fabricate medical endorsements, impersonate physicians to obtain confidential data, and create falsified treatment videos that distort public understanding of legitimate medicine, fueling distrust and vaccine hesitancy. 

As the paper warns, “The proliferation of deepfakes has triggered phenomena like ‘Impostor Bias,’ a skepticism toward the authenticity of multimedia content, further complicating trust in digital interactions.” That erosion of confidence is particularly dangerous in medicine.

The same technology also enables the falsification of patient data and the manipulation of medical imaging for fraud. AI can now fabricate entire health records or generate diagnostic images showing tumors that don’t exist, or hiding those that do. According to the FF4ALL research team, “Ensuring the authenticity of digital content is a challenge in multimedia forensics as deepfake technology continues to evolve and produce realistic synthetic media. 

Detecting manipulated content is helps mitigate the risks of misinformation, identity fraud, and media integrity threats while also serving as the foundation for forensic analysis, attribution, and authentication.” Yet detection systems remain locked in what the authors describe as “a dynamic and continuous arms race” against adaptable AI forgery techniques.

When such fabricated information seeps into healthcare systems, whether through falsified clinical trials, fraudulent insurance claims, or manipulated patient records, the consequences extend beyond the digital realm. False data can influence diagnoses, alter treatment plans, and mislead policymakers, placing patient lives and public trust at risk.

Why staff are vulnerable

Synthetic forgeries use advanced tools like GANs and neural network models to create audio, video, and document fakes that look and sound remarkably real. As mentioned in the above referenced Nursing Reports study technology can even generate “entire documents, including patient records, consent forms, and clinical reports, that appear authentic upon casual inspection.” 

A referenced study in the ursing Reports from the Catholic University of Valencia found that nursing students identified “21 descriptive codes, classified into four main themes: advantages, disadvantages, health applications, and ethical dilemmas.” Participants noted that benefits included the potential “use in diagnosis, patient accompaniment, training, and learning,” while perceived risks included “cyberbullying, loss of identity, and negative psychological impacts from unreal memories.”

Healthcare professionals, however, focus primarily on patient care and clinical decision-making, not on verifying digital authenticity. The study discusses how easy it is for misinformation to slip through, quoting one student who said, “it will be much more difficult to distinguish between what is truthful and what is not.” Another added that “a person who does not know much about DeepFakes has no reason to suspect that audio-visual content that looks real has been artificially produced.” 

These human vulnerabilities, combined with multitasking, stress, and time pressure, make staff more prone to trust familiar-looking content. Students echoed this concern in their testimonies: “In the health field, this is very worrying because it can easily confuse people and put their health at serious risk,” and “videos using DeepFakes would lead many people to believe that solutions or diagnoses for any kind of illness can be given by anyone, thus creating an uninformed society.”

How healthcare organizations can fight back

Healthcare organizations can counter deepfake and forgery risks through automated content verification powered by AI. These systems use algorithms trained to detect irregularities that indicate manipulation. Machine learning models such as deep neural networks apply image forensics and pattern recognition to analyze audio, video, and document files for signs of alteration. 

According to the Journal of Medical Ethics study indexed in the BMJ Open Access, deepfakes are “hyper-realistic videos digitally manipulated to depict people saying and doing things that never actually happened,” and they rely on “deep learning, a form of artificial intelligence (AI), to digitally manipulated to depict people saying and doing things that never actually happened (p.40).” Researchers warn that “the line between reality and fabrication can become blurry,” illustrating how easily false information can appear authentic in healthcare settings.

These tools can identify subtle indicators of synthetic media, including inconsistent lighting, unnatural lip movements, and pixel-level distortions, details that are often missed during routine clinical work. Embedding such verification tools into email or document systems enables screening of multimedia before it is shared or acted upon, reducing the likelihood of deception. 

HIPAA compliant email platforms like Paubox address this by using transport layer security (TLS) and encryption protocols to protect message integrity and data confidentiality. The systems also automate compliance monitoring to ensure that all messages meet HIPAA standards for handling protected health information (PHI). Together, these measures strengthen the reliability of digital communication and limit the potential for data exposure and misinformation.

FAQs

What is generative AI?

Generative AI refers to artificial intelligence systems designed to create new content based on the patterns they learn from existing data.

How does generative AI work?

Generative AI models rely on machine learning techniques, particularly deep learning. They use neural networks trained on large datasets to understand relationships between words, pixels, or sounds. One popular architecture is the GAN, where two models, a generator and a discriminator, compete to produce increasingly realistic outputs.

What role does generative AI play in cybersecurity?

Generative AI assists in both offense and defense. It helps cybersecurity teams simulate attacks, identify vulnerabilities, and automate response strategies. However, it can also be misused to create realistic phishing messages, fake identities, or voice deepfakes. Security experts recommend pairing AI-powered detection tools with human oversight to counter such threats effectively.