Deepfakes are synthetic media, usually videos, audio recordings, or images, that have been manipulated or generated using artificial intelligence (AI) to make it appear as though someone said or did something they never actually did. The term combines “deep learning” (a type of machine learning used in the creation process) and “fake.”
While often associated with entertainment or political misinformation, deepfakes are increasingly relevant to the healthcare industry, particularly in the context of privacy, security, and HIPAA compliance.
Deepfakes use AI techniques, especially deep learning and generative adversarial networks (GANs), to:
The study Exploring Deepfake Technology: Creation, Consequences and Countermeasures provides a comprehensive examination of deepfakes, demonstrating both their legitimate applications and the potential for misuse.
Furthermore, the above mentioned study, identified risks associated with deepfakes and categorized them into key areas:
Deepfakes can be used to fabricate videos of politicians or public figures, spreading false information and manipulating public opinion. During the 2018 Kenyan elections, for example, there was concern that deepfake videos were used to falsely portray a candidate as being in poor health.
This poses a threat to democratic processes and media credibility.
One of the most widespread and damaging uses of deepfakes is creating sexually explicit videos without the consent of the individual depicted. These can cause severe psychological harm, reputational damage, and legal complications, especially targeting women and public figures.
Deepfakes can be used to clone the voices or faces of individuals (e.g., CEOs or financial officers) to commit fraud.
Scammers have successfully tricked employees into transferring funds by impersonating executives using deepfake voice technology.
See also: Healthcare records: The top target for identity theft
As deepfakes become more realistic, it becomes harder to distinguish real content from fake, leading to a "liar's dividend" — where genuine media can be dismissed as fake, and fake content may be believed.
This undermines trust in journalism, legal evidence, and interpersonal communication.
Manipulated media could misrepresent patients or be used to share non-consensual images or recordings. This can lead to severe HIPAA violations and reputational harm for healthcare institutions.
Current laws often lag behind the capabilities of deepfake technology. HIPAA does not specifically address synthetic media, leaving providers and organizations to interpret how such threats intersect with existing privacy requirements.
Victims of deepfakes often suffer from emotional distress, humiliation, and damage to personal relationships.
The societal spread of such content also contributes to a toxic digital environment, especially on social media platforms.
Read also: Can deepfakes be beneficial in healthcare?
See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)
Laws vary by country. Some jurisdictions have laws against creating or distributing non-consensual deepfake pornography or using deepfakes for fraud. However, many areas still lack comprehensive legal frameworks addressing deepfakes.
Yes. Open-source software and mobile apps now allow almost anyone to create deepfakes with minimal technical skill. However, higher-quality deepfakes still require large datasets and computing power.
Some signs include: