Will artificial intelligence live up to its promises for rare diseases?
Will artificial intelligence live up to its promises for rare diseases?
Publish Date: 2026-05-08 04:48:00
Source Domain: www.openaccessgovernment.org
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AI in Healthcare Applications: AI is increasingly used by clinicians to support diagnosis, treatment selection, patient follow-up, and continuous education by processing vast scientific literature. It also aids in drug monitoring, supply chain management, and real-world data analysis for technology comparison.
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Opportunities vs. Oversight: While AI tools can diagnose diseases and even outperform healthcare professionals in some contexts, maintaining the ethical proposal of human oversight can be challenging. Strains on healthcare systems, like in the UK, may increase the dependency on AI to assist in diagnosis and treatment selection.
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Healthcare Access and Health Literacy: AI solutions, including medical chatbots, aim to improve health literacy. They offer potential solutions to simplify complex medical information which is often written at a level not understood by many patients. However, standardized medical literature often falls short for complex conditions like rare diseases.
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Limitations in Rare Diseases: AI performance in diagnosing rare diseases is hindered by the limited size and quality of training datasets. Efforts to enhance AI predictions in small datasets exist, but significant challenges remain in providing reliable AI solutions that complement specialized care for rare diseases.
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Patient Involvement and Trust: Patient engagement and trust are critical for AI adoption in healthcare. AI projects should involve patients from the beginning, and frameworks like CHEERS-AI and MAS-AI incorporate patient feedback. Despite benefits, significant proportions of people would still refuse certain AI interventions.
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Overall Reliability and Acceptance: The success of AI in healthcare will hinge on technical performance, transparency, governance, and meaningful engagement with end users. Adoption and acceptance are dependent upon the interplay of these factors.
