Nelson Advisors referenced in the Official Journal of the European Union paper ‘Opinion of the European Economic and Social Committee: AI, Big Data and rare diseases’
NA
Nelson Advisors referenced in the Official Journal of the European Union paper ‘Opinion of the European Economic and Social Committee: AI, Big Data and rare diseases’.
Opinion of the European Economic and Social Committee
AI, Big Data and rare diseases
(exploratory opinion requested by the Danish Presidency of the Council of the EU)
(C/2026/24)
4. Key challenges and risks
4.1.
Despite its promise, the application of AI and Big Data in rare disease research raises serious concerns about control by humans, data quality, data privacy, algorithmic bias, affordability and accessibility. An overreliance on AI may lead us to neglect a holistic approach to treatment strategies, and ethical considerations. AI may lead to rational decisions that are inappropriate for the patient’s situation as not all parameters are included in the AI models. The final decision must remain with the healthcare and medical profession.
4.2.
Ensuring equal access to AI-driven healthcare innovations:
4.2.1.
The EU Pharmaceutical Strategy for Europe emphasises the need to foster innovation in rare disease treatments and AI-driven drug discovery, especially for rare diseases that lack commercial incentives for research. These treatments are excessively priced and weigh heavily on health budgets.
4.2.2.
Rare disease data in Europe is scarce and fragmented, limiting large-scale AI analysis. Without standardised high-quality data, AI may only benefit well-represented diseases or wealthy populations. Access to AI tools is uneven, with rural-urban divides, digital health infrastructure gaps between countries and high treatment costs deepening inequalities. Ensuring access to quality data is essential for effective AI-driven treatments.
4.3.
Bias and gender disparities in AI models:
4.3.1.
Women are disproportionately affected by autoimmune and chronic rare diseases and wait an average of four years longer to receive a diagnosis for the same disease as men (18). Most preclinical studies use data from European-descent males (19), leading AI models trained on these datasets to misdiagnose women more frequently (20). An unfortunate trend in abusing women’s health data is also seen (21). AI-based health monitoring apps often track and store sensitive reproductive health data, raising privacy concerns (22).
Source: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:C_202600024
(20) Are AI tools failing women’s health?.
https://www.healthcare.digital/single-post/are-ai-tools-failing-women-s-health