Navigating the uncommon: challenges in applying evidence-based medicine to rare diseases and the prospects of artificial intelligence solutions.

Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 9815900 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1572-8633 (Electronic) Linking ISSN: 13867423 NLM ISO Abbreviation: Med Health Care Philos Subsets: MEDLINE

Original Publication: Dordrecht ; Boston : Kluwer Academic Publishers, c1998-

Rare Diseases*/therapy ; Artificial Intelligence* ; Evidence-Based Medicine*; Humans ; Research Design ; Algorithms

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no 'one-size-fits-all' approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models.
(© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

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Keywords: Artificial intelligence; Ethics; Evidence-based medicine; Machine learning; Philosophy of medicine; Rare disease

Date Created: 20240509 Date Completed: 20240808 Latest Revision: 20240808

20240808

10.1007/s11019-024-10206-x

38722452