arXiv:2603.18577v2 Announce Type: replace Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of reali
The proliferation of advanced AI image editors makes high-fidelity medical deepfakes a pressing concern, requiring immediate defensive innovation.
Ensuring the integrity of medical data is crucial for patient safety and maintaining trust in AI-driven healthcare systems, directly impacting clinical practice and regulatory frameworks.
The introduction of MedForge provides a specialized, interpretable solution for medical deepfake detection, moving beyond black-box and post-hoc methods.
- · Healthcare providers
- · Medical AI developers
- · Patients
- · Regulatory bodies
- · Malicious actors
- · Current generic deepfake detection systems
- · Medical data fraudsters
Improved detection capabilities for manipulated medical scans will bolster trust in digital medical records and AI diagnostics.
This will drive the development of more robust, secure AI healthcare solutions and potentially new standards for medical imaging authentication.
The heightened security in medical imaging could accelerate the adoption of telehealth and remote diagnostics, transforming healthcare delivery models.
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Read at arXiv cs.AI