Advances in predictive medicine — genetic testing, AI‑assisted health analytics and wearable monitoring — can now produce information not only about present illness but about future health risks. Such data may indicate an elevated probability for diseases like cancer, Parkinson’s disease, Alzheimer’s disease or certain mental illnesses.
The central question is what happens if employers obtain these predictions and use them in hiring, promotion, or workforce planning.
What studies and practice are showing
In the United States, an increasing number of companies include genetic testing in employee wellness programs. A 2025 nationwide survey found substantial employee interest in workplace genetic testing: nearly 60% of respondents agreed that genetic testing can contribute to health protection, and about half said such tests could help attract and retain staff.
At the same time, respondents voiced strong privacy and discrimination concerns: 37% would feel uncomfortable if their employer could access such data, and eight out of ten (80%) were somewhat or very worried that sensitive information would not be properly protected or might be shared without consent.
In Hungary there are also health providers offering continuous, individualized integrated data analysis within longevity programs, monitoring hundreds of biomarkers. These services promise tailored interventions and longer, healthier lives, while collecting large volumes of sensitive personal data.
Law, science and professional debate — regulation lags behind
Polygenic risk scoring — which aggregates many genomic factors into a risk estimate — is becoming more widespread. Unlike single‑gene tests that detect clear pathogenic mutations, these scores produce cumulative risk estimates that are harder to interpret.
Legal frameworks often struggle to keep pace with technological progress. Sam Trejo, a sociologist at Princeton University, warned in The New York Times that U.S. protections against genetic discrimination (notably the GINA law) may be insufficient to cover scenarios emerging in coming decades. Even within medicine there is debate about how to handle and report polygenic risk scores; without clear professional consensus, it is especially problematic if such results inform workplace decisions.
Researchers at Harvard and Yale have noted that while a company currently cannot fire a healthy employee solely because of an unfavorable polygenic test result, it may still deny the employee requests for less demanding roles. Thus, decisions that appear objective could have discriminatory outcomes.
EU action and technological limits
The European Union has started setting boundaries: the AI Act guidance published in 2025, for example, prohibits systems that monitor or evaluate employees’ emotional states at work. The aim is to limit harms that can arise from aggregating health and physical data: more sensitive data does not automatically mean better decisions and can increase the risk of misuse.
The main risk: invisible discrimination during recruitment
Experts argue the greatest danger from predictive health data may occur during hiring. Consider two equally qualified candidates: if an algorithm flags one as having a higher future risk of a neurodegenerative illness, that candidate may be disadvantaged in selection processes, even if the prediction never materializes.
What companies can do
Specialists recommend three core principles for responsible employers:
- Data minimization: not every technically obtainable data point is necessary or useful for HR processes.
- Transparency: employees must know what health data are collected, how they are used and who can access them.
- Human oversight: AI‑based predictions must not become the sole basis for automated HR decisions.
Following these principles can reduce misuse and discriminatory outcomes, but they do not eliminate the need for updated legal and ethical standards and ongoing dialogue between professionals, regulators and employers.
Conclusion
Predictive medicine can bring clear benefits for prevention and individual health, but it also creates serious challenges for the workplace. Until data use practices and legal frameworks are clarified, the principal risk lies in misinterpretation and secondary effects of sensitive health information in recruitment, promotion and career planning. Responsible corporate policies, strict data governance and preserved human decision‑making are essential to ensure technological advances do not translate into systemic disadvantages for employees.
