This research systematically analyzes the security and privacy risks of AI in healthcare, revealing major vulnerabilities across diagnostic and predictive systems while highlighting under-explored attack vectors and urging stronger defenses.
In today’s hospitals and clinics, artificial intelligence (AI) is more than a buzzword — it’s saving lives. From diagnosing diseases through images to predicting patient outcomes, AI tools are changing the healthcare game. But here’s the catch: as machines get smarter, so do the threats.
A new study from Washington University shines a spotlight on a growing concern: security and privacy risks in healthcare AI. Think of it like this: would you want your MRI scan or health history exposed in a cyberattack? Or your diagnosis manipulated for profit? No way!
Let’s break down this important research into simple terms and explore how we can build trustworthy, secure AI systems for healthcare.
By 2030, the healthcare AI market is expected to skyrocket to $188 billion. Already, AI is used to:
Surveys show that 44% of people are open to AI-driven medical decisions, and 24% of healthcare organizations are testing AI models today.
Sounds promising, right? But beneath this excitement lies a serious blind spot: security and privacy.
The researchers reviewed over 3,200 biomedical papers and 101 AI security studies. They found a surprising imbalance:
Even worse, biomedical experts and security researchers don’t talk to each other enough. The threat models used in AI security often don’t match real-world healthcare risks.
Here’s a cast of characters who might misuse healthcare AI:
Each has different access and goals — from stealing private data to corrupting diagnoses.
The study organizes attacks into three main categories:
Manipulate the AI’s output — like changing a “cancer” diagnosis to “healthy.”
Steal sensitive patient data.
Shut down or overload the system.
This isn’t just theory. The authors ran proof-of-concept attacks in under-explored areas like:
BiomedCLIP is a multi-modal AI model (uses images + text). By slightly altering the image, the researchers could drastically reduce diagnostic accuracy — down to just 5%!
They implanted a subtle “trigger” signal in ECG time series. Result? The AI misdiagnosed normal heartbeats as dangerous — while seeming normal otherwise.
Even with limited knowledge, attackers could guess which patients’ data trained the model. Not perfect — but enough to raise serious privacy concerns.
In tests using real-world health data (like MIMIC-III), attackers flipped labels and injected noise — making models less reliable without obvious signs of attack.
Here’s what the authors recommend — and what engineers and researchers should work on next:
Move beyond just image-based attacks. Study neglected domains like:
Develop generalizable defenses across data types. Think of:
Bridge the gap between:
The goal is to design systems that are both intelligent and reliable.
Regulatory frameworks like TRIPOD-AI and CONSORT-AI don’t currently require security analysis. That needs to change.
For engineers working on healthcare AI, here’s your checklist:
Healthcare AI promises better, faster, fairer medical care. But without robust security and privacy, that promise could backfire.
This research shows we need to rethink how we design, test, and deploy AI in medical settings. It’s not just about saving lives — it’s about protecting them too.
So next time you build a medical AI model, ask yourself:
“Can I trust this AI with my life — and my data?”
When a clear 'yes' is not the immediate answer, it's necessary to engineer more effectively.
Security - Keeping systems safe from being tampered with — think of it as protecting the function of AI from hackers or bad actors. - More about this concept in the article "Securing the Future: Cybersecurity Threats & Solutions for IoT-Integrated Smart Solar Energy Systems".
Privacy - Keeping personal info (like your medical history) safe and secret — it’s about protecting the data.
Adversarial Attack - A sneaky trick where small changes fool an AI into making the wrong decision — like making a cancer scan look healthy. - More about this concept in the article "Unlocking the Black Box: How Explainable AI (XAI) is Transforming Malware Detection".
Federated Learning - A way for hospitals to train AI together without sharing patient data — the model travels, not the data. - More about this concept in the article "The GenAI + IoT Revolution: What Every Engineer Needs to Know".
Evasion Attack - An attack that changes the input slightly (like a medical image) so the AI gets the answer wrong — without you noticing!
Poisoning Attack - Messing with the training data so the AI learns the wrong thing — like teaching a dog to sit when you say “run.”
Backdoor Attack - Installing a hidden “trigger” during training — when it sees a specific signal, the AI behaves in a dangerous or wrong way.
Membership Inference - A way hackers try to guess if your personal data was used to train the AI — a privacy red flag.
Electronic Health Records (EHR) - Digital versions of your medical files — from blood pressure readings to allergy lists. - More about this concept in the article "Generative AI in Medicine: Revolutionizing Healthcare with Machine Learning".
Yuanhaur Chang, Han Liu, Chenyang Lu, Ning Zhang. SoK: Security and Privacy Risks of Healthcare AI. https://doi.org/10.48550/arXiv.2409.07415
