Healthcare AI: Addressing Liability, Data Privacy, and Social Justice

The implementation of healthcare artificial intelligence (AI) can revolutionise and potentially solve issues in the healthcare sector. With growing patient demand, largely due to an ageing population and an increase in chronic illnesses, a shortage of 4.1 million healthcare workers is estimated by 2030. Hence, streamlining hospital workflows and increasing productivity in pharmaceutical and medical device companies will be essential for meeting the rising demand for a high-quality healthcare system. AI has the potential to speed up and improve diagnostics, enable more personalised care, and transform drug discovery from a labour-intensive into a capital and data-intensive process. However, the medical industry has been reluctant to implement AI where necessary due to 3 main reasons: complicated liability structures, data privacy issues, and potential social justice concerns. This article will discuss each reason in turn.

Complex Liability Structure

Liability in AI-powered diagnosis, drugs, and medical devices entails a complex chain of actors. In the event of a false diagnosis resulting from an AI model in a medical device, the device manufacturer may be held strictly liable under the Product Liability Directive (PLD). Yet, the manufacturer can also seek compensation from the AI provider if the error is due to the latter’s development process. Moreover, doctors ultimately remain legally accountable for clinical decisions, even when AI tools are used. Therefore, overlapping AI ecosystems complicate the liability structure, potentially discouraging medical professionals from effectively implementing healthcare AI.

To address the challenge of complex liability in healthcare AI, regulators should establish a clearer allocation of responsibility across different actors in the AI ecosystem. One potential approach is to introduce specific liability frameworks tailored for AI in healthcare, similar to medical malpractice laws, but adapted to account for AI’s involvement. For example, device manufacturers and AI developers could be required to implement rigorous testing, transparency, and traceability measures, ensuring that errors can be attributed to the appropriate party. Additionally, regulatory “safe harbours” could be introduced for medical professionals, protecting them from disproportionate liability when AI tools are used in good faith and in compliance with established standards. This would give doctors more confidence in using AI systems whilst maintaining accountability for manufacturers and developers.

Data Privacy Issues

Protecting patients’ data privacy when using healthcare AI is paramount for ethical medical practice. AI developers and medical professionals must comply with data protection responsibilities and human/consumer rights at every stage of the process.

Following key principles of data protection—including purpose limitation (only using collected data for specified purposes), data minimisation (only collecting data that is necessary), data anonymisation, and the right of individuals to know how their data is used—will be essential for protecting patients’ privacy rights. Obtaining patient consent when collecting data is also necessary for informed consent. Therefore, companies and individuals in the healthcare industry must ensure that all patient data is used in an ethical, responsible, and productive manner.

Data privacy concerns can be mitigated by strengthening existing data protection frameworks to explicitly address AI applications in healthcare. Legal reforms could require developers to incorporate “privacy by design,” ensuring that anonymisation, encryption, and limited data retention are built into AI systems from the outset. Patients’ rights can be further safeguarded by mandating clear, standardised consent procedures that are easy to understand and revoke.

Cross-border data sharing, which is increasingly common in AI training, could also be governed by international agreements (perhaps unlikely) that harmonise privacy protections and prevent “forum shopping” by companies seeking weaker jurisdictions. These measures would balance innovation with the protection of patient rights.

Issues Around Social Justice

Another key issue in healthcare AI is the risk of increasing social inequality in the existing healthcare system. AI models can perpetuate social biases, especially if trained on non-representative datasets. Bias in clinical AI risks excluding underserved populations, as exemplified by the case of darker-skinned women being more likely to be misidentified in facial recognition systems.

Legal standards should mandate diverse, representative datasets in healthcare AI to reduce bias and inequality. Regulatory bodies could mandate fairness audits and independent testing before AI systems are approved for clinical use.

Additionally, clinical trial regulations may be updated to require the inclusion of historically underserved populations, ensuring that AI tools do not systematically disadvantage certain groups. Anti-discrimination laws could be expanded to explicitly cover algorithmic decision-making in healthcare, allowing affected individuals to seek legal remedies when harmed by biased AI systems. These steps would encourage the responsible use of AI whilst promoting equity and inclusivity in healthcare delivery.

Conclusion

Healthcare AI holds transformative potential, but will only be trusted and adopted widely if legal frameworks evolve to address liability, data protection, and social justice concerns. By introducing clear rules, enhancing transparency, and embedding fairness into AI governance, policymakers can foster an environment where AI innovation thrives whilst safeguarding patient rights and social equity.

Image by Ecole polytechnique via WikimediaCommons