You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

Abstract

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools

“…The teleconference was useful to clarify answers and explain why certain information was unknown or not able to be disclosed. Reporting caveats for deployment in the survey was difficult for one participant, similar to a previous observation [ 33 , 34 ], but the caveats could be clarified during the teleconference. We acknowledge that our survey may require adaptation to assessors or stakeholders who have different requirements of transparency [ 33 ].…”

“…One company could only perform a limited technical validation because the prediction target was rare, and only a few medical experts could validate the correctness of the predictions. Another company was unable to conduct a fairness assessment across the subgroups, because the demographic metadata was lacking due to data protection, which supports the argument that the potential of medical AI can only be realized if countries specify the right balance between data privacy and data access conditions [ 34 ]. Both the transparency and trustworthiness scores from Section 5 on technical validation and Section 6 on caveats for deployment reflected the completeness of the quality assessment lifecycle outlined in [ 32 ].…”

Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics

“…Recent advances in Big Data and Artificial Intelligence (AI) have provided researchers with unprecedented capability to model complexities of real-world EHR data (1)(2)(3)(4)(5). Yet legitimate concerns about patient privacy and ethical AI have limited the availability of EHR data within the broader AI research community (6)(7)(8). The use of synthetic data, which partially mimics the statistical properties of real data (9,10), reduces the restrictions to sharing research results and analysis across multiple organizations (11).…”

Artificial Intelligent Tools: Evidence-Mapping on the Perceived Positive Effects on Patient-Care

“…Though the continent is far from realising full adoption of AI applications in all aspects of their healthcare systems, there are modest gains in most parts of the continent [18,19]. For instance, funding for research projects in AI adoption in healthcare through European Union Horizon 2020 scheme shot up between 2014 to 2020 [20]. Moreover, the European Commission developed several ethicolegal instruments to regulate and guarantee responsible design and use of AI systems in patient-care and beyond [16,20].…”

“…For instance, funding for research projects in AI adoption in healthcare through European Union Horizon 2020 scheme shot up between 2014 to 2020 [20]. Moreover, the European Commission developed several ethicolegal instruments to regulate and guarantee responsible design and use of AI systems in patient-care and beyond [16,20].…”