Are Healthcare AI Models Trained with My Health Data?
- David Priede, MIS, PhD
- Apr 23
- 6 min read
Updated: Sep 9
How healthcare AI models are trained using patient health data, the privacy safeguards in place, and what this means for you.
WHY IS IT IMPORTANT? |
This article is important because it helps readers understand how their health data may be used to train artificial intelligence in healthcare, highlighting the need to balance the benefits of AI-driven medical advances with the protection of patient privacy and ethical data use—a topic that is increasingly relevant as AI becomes more integrated into hospitals and clinics, raising complex questions about consent, data security, and regulatory compliance. |
Takeaways
Healthcare AI models are trained using data from electronic health records (EHRs) and other patient sources.
Data privacy laws, such as the Health Insurance Portability and Accountability Act (HIPAA), regulate the use of health information for AI training.
Most data used for AI is anonymized or pseudonymized; however, risks of re-identification still exist.
Patients’ data may be included in AI training if it is part of a healthcare provider's records, provided that consent and applicable legal requirements are met.
Data quality and bias are major concerns in training healthcare AI models.
Introduction
Artificial intelligence is transforming healthcare, promising more accurate diagnoses, personalized treatments, and streamlined hospital operations. But have you ever wondered if your health data is being used to train these powerful AI models? Understanding how and why your personal information might be involved is essential for anyone who visits a doctor or uses a health app.
In this article, I’ll explain how healthcare AI models are trained, what data is involved, the privacy protections in place, and what this means for you as a patient.
How Healthcare AI Models Are Trained
Healthcare AI models, including those that interpret medical images or predict patient outcomes, require vast amounts of data to “learn” patterns and make accurate predictions. Most of this data originates from electronic health records (EHRs), which contain information such as diagnoses, treatments, laboratory results, imaging studies, and occasionally, doctors’ notes.
Example: A hospital might use thousands of anonymized chest X-rays and their associated diagnoses to train an AI model to detect pneumonia or lung cancer. The more diverse and comprehensive the data, the better the AI can learn to recognize subtle differences.
Real-World Application |
Large health systems often partner with technology companies to develop AI tools that can, for example, identify patients at high risk for complications after surgery. |
What Kind of Data Is Used?
The data used to train healthcare AI models comes from various sources:
Electronic health records (EHRs)
Medical images (X-rays, MRIs, CT scans)
Lab results and genetic data
Doctor’s notes and discharge summaries
Wearable device data (in some cases)
Most of this data was not initially collected for AI development, but rather for patient care. It is then extracted, cleaned, and sometimes anonymized or pseudonymized before being used for AI training.
Example: A research project might use de-identified EHR data from thousands of patients to develop a model that predicts diabetes complications.
About 80% of healthcare data is unstructured, such as free-text notes, making it challenging to prepare for AI training.
Is My Personal Health Data Used?
If you have received care at a hospital or clinic, your data may become part of the large datasets used to train AI models. However, several safeguards and regulations are in place:
Consent: Many projects require patient consent to use identifiable health data for research or AI development.
De-identification: Data is often stripped of direct identifiers (such as name and date of birth) to protect privacy. However, sophisticated AI can sometimes re-identify anonymized data if combined with other information.
Legal Restrictions: In the United States, HIPAA and other privacy laws strictly regulate the use of protected health information. Only “covered entities” (such as hospitals) or their partners can use health data for AI, and typically only for treatment, payment, or healthcare operations, unless explicit consent is obtained.
Example: A hospital may use de-identified patient data to develop an AI tool for internal use, but it cannot sell or share that data without meeting strict legal requirements.
Privacy Risks and Safeguards
While privacy laws provide a framework, risks remain:
Re-identification: Advanced AI can sometimes match de-identified data to individuals, particularly when dealing with rare diseases or unique data combinations.
Data Breaches: Large datasets are attractive targets for hackers.
Bias: If the training data is not representative, AI models may perform poorly for specific groups, resulting in unequal care.
Safeguards:
Data is often anonymized or pseudonymized before use.
Hospitals and companies must follow strict data governance and security protocols.
Regulatory bodies, such as the FTC and state agencies, monitor and enforce data privacy in AI development.
The Federal Trade Commission (FTC) has ordered companies to destroy AI models trained on improperly collected data, a measure known as "algorithmic disgorgement". This action is meant to penalize companies that benefit from ill-gotten data, emphasizing that existing consumer protection and privacy laws apply to AI technologies.
Why Is Data Quality So Important?
The saying “garbage in, garbage out” is especially true for healthcare AI. Poor-quality or biased data can lead to inaccurate or even dangerous recommendations.
Data Cleaning: Before training, data must be standardized and checked for errors.
Bias Mitigation: Developers must ensure the training data reflects the diversity of the population the AI will serve.
Continuous Monitoring: AI models are regularly evaluated and updated as new data becomes available.
Example: If an AI model is primarily trained on data from a single ethnic group, it may not perform well for other groups, potentially leading to misdiagnoses.
How Can Patients Control Their Data?
Patients have certain rights regarding their health data:
Access: You can request access to your health records.
Amendment: You can ask for corrections to your data.
Consent: For many research and AI projects, your explicit consent is required, particularly for data that can be identified.
Opt-Out: Some healthcare systems allow you to opt out of having your data used for research or AI development.
HIPAA and other privacy laws give patients the right to know how their data is used and to request limitations on its use.
Conclusion
Healthcare AI models are often trained using large datasets that may include your health information, typically extracted from electronic health records. Strict privacy laws and technical safeguards are in place to protect your identity; however, risks such as re-identification and bias persist. Patients have rights to access, amend, and sometimes opt out of data use, ensuring a balance between innovation and privacy.
The use of health data to train AI models is helping drive significant advances in medicine, from faster diagnoses to more personalized care. While your health information may contribute to these breakthroughs, robust privacy laws and ethical standards are designed to protect you. As AI becomes more integrated into healthcare, ongoing vigilance and transparency are needed to maintain trust and ensure these tools serve everyone fairly.
Frequently Asked Questions
1. Can I find out if my specific health data was used to train an AI model?
Generally, it is not easy to determine if your data was included, especially if the data was de-identified and used in large, aggregated datasets. Healthcare providers may offer information about their data use policies.
2. What happens if I don’t want my health data used for AI training?
You can ask your healthcare provider about opt-out options or data use policies. For many research projects, your explicit consent is required, particularly for data that is identifiable.
3. Are AI models ever trained on data from health apps or wearables?
Yes, some AI models use data from consumer health devices or apps; however, this data is also subject to privacy laws and regulations, particularly when it is linked to identifiable information.
4. How do companies ensure AI models don’t “learn” private information?
They use anonymization, strict data governance, and security protocols. However, there is always a small risk of re-identification, especially with advanced AI techniques.
5. What should I do if I suspect my health data was misused in AI training?
Contact your healthcare provider or the relevant regulatory authority, such as the U.S. Federal Trade Commission (FTC), to report concerns and seek guidance.
Sources
Frost Brown Todd. (2024). Beware Privacy Risks In Training AI Models With Health Data.
Infiuss Health. The Role of Data in Healthcare AI Training.
National Center for Biotechnology Information. Is there a civic duty to support medical AI development by sharing ...
Wolters Kluwer. Preparing Healthcare Data for AI Models.
MedCity News. 'Garbage In Is Garbage Out': Why Healthcare AI Models Can Only Be As Good As The Data They're Trained On.
About Dr. David L. Priede, MIS, PhD
As a healthcare professional and neuroscientist at BioLife Health Research Center, I am committed to catalyzing progress and fostering innovation. With a multifaceted background encompassing experiences in science, technology, healthcare, and education, I’ve consistently sought to challenge conventional boundaries and pioneer transformative solutions that address pressing challenges in these interconnected fields. Follow me on Linkedin.
Founder and Director of Biolife Health Center and a member of the American Medical Association, National Association for Healthcare Quality, Society for Neuroscience, and the American Brain Foundation.
