Synthetic Patients, Real Ethics: Why Nurses Must Shape the Future of Healthcare AI Training Data

Healthcare is moving fast toward synthetic data. Systems now generate artificial patient records to train AI models without exposing real identities. This approach promises privacy and scale, but it brings new risks. Synthetic patients still reflect real-world bias, missing context, and broken workflows. Nurses need a leading role in how synthetic data is created, reviewed, and used. Safety depends on it.

What Synthetic Data Is

Synthetic data is artificial data generated by statistical models or machine learning systems. It looks like real clinical data, but does not come from actual patients. Teams use it to train AI tools, run simulations, and expand datasets when real data is limited.

A 2025 paper by Nisevic, Milojevic, and Spajic described synthetic data as applicable for privacy and research access, but warned that synthetic patient profiles still raise ethical and legal concerns.

Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC12166703/

Healthcare uses synthetic data to reduce risk. It avoids direct exposure of protected health information. It supports model development when real datasets are small. It helps teams test ideas without waiting for sample sizes to grow.

The Ethical Risks Behind Synthetic Patients

Synthetic data is not neutral. It inherits the structure of the real data used to create it. When the source data is biased, incomplete, or unrepresentative, the synthetic version repeats those flaws.

A Nature report published in 2025 showed that institutions sometimes bypass ethics review for AI-generated datasets, assuming they are risk-free. This assumption is false.

Reference: https://www.nature.com/articles/d41586-025-02911-1

Synthetic data also lacks clinical nuance. Vitals, labs, and codes generate clean patterns. Nursing notes, emotional cues, tone of conversation, physical behavior, and social context often do not. Models trained only on structured synthetic data miss these details.

Privacy risk is also not zero. Some synthetic datasets can be reverse-engineered if the generation methods are weak. A 2024 analysis of synthetic health data warned that unsafe generation processes can re-create patient-level signals.

Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC11555762/

Why Nurses Must Be Involved

Nurses see the gaps that synthetic data cannot capture. They recognize early deterioration that does not appear in vitals. They understand how patient behavior shifts recovery. They know how documentation errors distort a clinical picture.

If nurses are not involved in shaping synthetic data pipelines, AI models will be trained on incomplete stories. Nursing knowledge protects against narrow, data-only views of patient care.

Nurses bring three strengths that synthetic data alone cannot provide.

They know where real data breaks—missing pain scores. Late vitals. Incomplete assessments. These gaps matter when building synthetic versions.

They understand the meaning behind patterns. A stable vital sign with anxious behavior is not stable in reality. Synthetic datasets often remove these contradictions.

They protect equity. Nurses see how different groups experience care. They know where bias enters the system.

A Framework for Nurse Involvement

Nurses and leaders can shape the use of synthetic data through direct action.

Review the source data. Ask what real data was used to build the synthetic dataset. Check whether it represents the patient groups you serve.

Check representation. Look for age, race, sex, condition, and socioeconomic patterns. Ask whether these distributions match the real population.

Advocate for unstructured data. Push for inclusion of nursing notes, free text, and context fields when possible. These elements capture meaningful clinical detail.

Monitor model behavior. Track where models trained on synthetic data fail. Review overrides and unexpected errors. Use these findings to adjust the dataset.

Demand governance. Participate in data councils, AI ethics groups, and informatics committees. Synthetic data is not “technical only” work. It shapes safety, and nursing leaders must guide it.

Case Example

You evaluate a falls-risk model trained with synthetic data from a general inpatient population. The model under-predicts risk in younger surgical patients. Your team reviews the synthetic dataset and finds that there are too few examples of this group. You push for expanding the dataset and adding structured fields to capture mobility, dizziness, and postoperative fear. Performance improves, and nurses gain trust in the updated tool.

Why This Matters

AI trained on synthetic data will influence real patients. If the training data is shallow, biased, or lacks clinical context, the model behaves similarly. Nurses protect patient safety by guiding how synthetic data is built and used. Leadership must elevate nursing voices in AI design now, not later.

References

Nisevic M, Milojevic D, Spajic D. Synthetic Data in Medicine: Legal and Ethical Considerations for Patient Profiling. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12166703/

Extance A. AI-generated medical data can sidestep usual ethics review, universities say. Nature. 2025. https://www.nature.com/articles/d41586-025-02911-1

Chen J, Zhang Z. Synthetic Health Data: Real Ethical Promise and Peril. 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11555762/

Ethics of AI in Healthcare: A Scoping Review. Frontiers in Digital Health. 2025. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1662642/full