Clinical trials are a key area where the use of AI in healthcare data has seen significant growth, but this introduces unique legal challenges. AI development often relies on data collected in clinical trials to train algorithms, which requires careful consideration of consent, data origin and ethical standards. When data is acquired from third-party sources, transparency on collection methods, geographic origin and anonymization standards becomes essential. Although consent forms used in clinical trials may offer clearer conditions for data use, ambiguity remains over how such data can be reused for AI purposes after the trial ends .
One of the main concerns relates to data re-identification, in which anonymized data can potentially be traced back to individuals, particularly when linked to other datasets. Data ownership is also a complex and often ambiguous area within the healthcare industry, particularly in the United States. AI developers must clearly explain the value of data collection to hospital cybersecurity teams, ensuring they understand how the data will be used securely and ethically.
Accuracy, fairness and robustness of data are essential
The European Union AI Law presents a compelling model for regulating AI using a risk-based approach. In this framework, health data falls into a “high risk” category, requiring strict quality controls and ethical standards. This approach emphasizes the accuracy, fairness, and robustness of data, addressing many concerns regarding the ethical implications of using AI in healthcare. The risk-based model is particularly well suited to managing the complexity of healthcare data, ensuring a high level of data quality without hindering innovation.
Cybersecurity is another critical issue, as healthcare data is particularly vulnerable to breaches, which can lead to identity theft, fraud and other risks. Ethical concerns also come into play, as inappropriate use of health data can lead to public backlash and erode trust in health care providers and AI technologies.
Given these complexities, experts at the panel discussion titled “Unlocking Health Data: Navigating the Legal Landmines for Innovation” at the MEDTECH 2024 conference held in Toronto, Canada, offered several recommendations.
Clear ownership rights enable responsible data sharing
It is essential to develop clear data ownership frameworks. Establishing clear guidelines regarding data ownership, particularly for clinical trials and healthcare provider data, will promote transparency and reduce conflicts over data use. Clear property rights can provide a basis for responsible data sharing practices.
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Improved communication with healthcare providers is equally crucial. AI developers should engage in open communication, explaining technical and regulatory terms in a way that is accessible to hospital staff and cybersecurity teams.
Adopting a risk-based regulatory approach, similar to the EU AI law, would create a flexible but high-level regulatory environment. This approach would classify the use of health data as high risk, imposing strict controls while promoting technological advancements.
Public education on data security and ethics is also vital. Educating the public about data security practices and the ethical use of AI can help build trust and address common concerns around data privacy and AI. Public awareness initiatives can clarify why certain data is collected and how it is protected, thereby building trust in these technologies.
