A deepfake is a fake media file, like an image, video, or sound, produced by AI processes such as deep learning to accurately alter or fabricate content. Advanced manipulations are used to spread misinformation, impersonate, or deceive people by making synthetic media appear genuine.
Deepfakes, fueled by deep learning and machine learning, are often discussed in the context of political, entertainment, and financial misinformation.
However, “Deep fakes pose an especially significant threat in the medical field because it deals with human lives, and any mistake or error could lead to a chain of terrible events,” explains Tech Science’s research study on deepfakes in healthcare.
Medical deepfakes can range from fake medical records and diagnostic pictures to simulated study data and pretended doctor credentials. Ultimately, they compromise patient safety, undermine confidence in healthcare organizations, and impact medical decision-making.
Deepfake technology can also create or forge medical images, like MRI scans, X-rays, and CT scans. A deepfake algorithm could employ these to add or delete markers of disease, leading to inaccurate diagnoses.
For instance, an attacker can forge cancerous tumors in a patient's scan, leading to unwanted procedures, or delete a tumor, leading to delayed treatment and even death.
If an AI model is trained on falsified information, even its own predictions can become unsustainable, especially as more hospitals and clinics integrate AI-assisted decision-making in their operations.
When an unauthorized individual accesses a patient’s medical history in the electronic health record (EHR), it could lead to inappropriate treatments and insurance scams. Like, when an individual alters their medical history to show a chronic illness, they can use this information to commit insurance fraud.
Deepfakes also threaten medical research, where scientists rely on accurate data collection. In clinical trials where datasets created by deepfakes are used, results could be unreliable, leading to misleading conclusions and incorrect therapies.
For example, a fraudulent study using tampered data as evidence of a new drug's efficacy could lead a toxic or ineffective drug onto the market, compromising patient safety.
Similarly, deepfake technology will also influence medical training. Surgical operation videos and training packages can be doctored, spreading incorrect techniques and false information among medicine professionals and students.
“Since deepfakes are a product of deep learning, it seems only reasonable to use deep learning and machine learning to combat them,” the study states.
Currently, advanced AI models are being developed to analyze discrepancies in image, video, and text data that the human eye can’t detect.
However, just like with every deep learning model, performance is a work in progress as more data in training is obtained to move closer to perfection with the model; this is true for the algorithms producing deepfakes and those that identify them.
So, models identifying medical deep fakes must stay alert and constantly retrain themselves to stay current with updates. Moreover, it calls for better awareness and aggressive technical counteraction in the health sector.
Along with technological solutions, regulatory frameworks must be strengthened to discourage and penalize deepfake technology in healthcare. Governments and healthcare regulatory bodies must implement verification processes for medical records, imaging data, and research publications.
Hospitals and medical centers must deploy AI-based authentication technologies to detect tampering and maintain data integrity. Moreover, these organizations must establish ethical codes and train medical professionals on digital falsification.
Learn more: Using AI ethically in HIPAA compliant email
Yes, hospitals can use AI-based authentication tools to detect tampered medical records, imaging data, and research documents.
Depending on the jurisdiction, penalties can include fines, medical license revocation, or criminal charges for fraud and endangering lives.
Yes, using deepfakes can erode trust in medical institutions, leading to skepticism about diagnoses and treatments.
Related: How HIPAA compliance improves patient trust