Can AI Make Decisions About Rare Diseases? A Critical Examination

The Promise and Peril of Artificial Intelligence in Orphan Conditions

The journey to a diagnosis for a rare disease—a condition affecting fewer than 1 in 2,000 people—is often referred to as a "diagnostic odyssey." This prolonged period of uncertainty, which can span years, is a significant source of patient suffering and a major challenge for healthcare systems globally. In this context, Artificial Intelligence (AI) has emerged not just as a tool, but as a potential paradigm shift, promising to cut years off this process. But the question remains: can AI truly make decisions about rare diseases, or is its role more nuanced?

AI's Transformative Role in Diagnosis

The primary application of AI in the rare disease space is in diagnosis, where its ability to process vast, complex datasets far surpasses human capacity [1]. Rare diseases are often characterized by a heterogeneous mix of subtle symptoms, genetic variations, and imaging anomalies. AI models, particularly those leveraging Machine Learning (ML) and Deep Learning (DL), excel at identifying patterns that are invisible or too dispersed for a clinician to connect manually.

Key areas where AI is proving transformative include:

1. Genomic Analysis: AI algorithms can rapidly analyze whole-exome or whole-genome sequencing data, filtering through millions of genetic variants to pinpoint the single, causative mutation. This drastically reduces the time required for genetic interpretation.
2. Phenotype Analysis: Tools like facial recognition software, combined with DL, can analyze subtle dysmorphic features in patient photographs to suggest a diagnosis. Similarly, AI can analyze medical images (MRI, CT scans) to detect subtle, disease-specific biomarkers [2].
3. Clinical Decision Support Systems (CDSS): AI-powered CDSS integrate electronic health record (EHR) data, patient history, and the latest medical literature to generate a ranked list of potential rare disease diagnoses, guiding the clinician's investigation.

These applications demonstrate AI's powerful capability to act as a diagnostic accelerator and a highly sophisticated decision-support tool.

The Leap from Decision Support to Autonomous Decision-Making

While AI is highly effective at suggesting a diagnosis, the concept of it making an autonomous decision about a rare disease is a subject of intense academic and ethical debate. The distinction between decision support and decision-making is crucial, especially in the context of orphan conditions.

Decision Support: AI provides a probability score or a ranked list of options. The final choice—the diagnosis, the treatment plan, or the next investigative step—rests with the human clinician. Decision-Making: AI autonomously selects a course of action that is then executed without human veto.

For rare diseases, the transition to autonomous decision-making is fraught with challenges [3]:

• Data Scarcity and Bias: By definition, rare diseases have limited patient data. AI models trained on small, non-diverse datasets are prone to overfitting and may fail catastrophically when presented with a novel presentation. This inherent data scarcity limits the model's generalizability and reliability for definitive decision-making.
• The "Black Box" Problem: Many powerful AI models, particularly deep neural networks, operate as "black boxes," making it difficult to trace the logic behind a suggestion. In rare diseases, where a single misdiagnosis can have devastating consequences, clinicians require explainability to trust and validate the AI's output.
• Ethical and Legal Liability: If an AI system makes an incorrect decision that harms a patient, the question of liability—who is responsible: the developer, the hospital, or the prescribing physician—is currently unresolved [4]. This legal ambiguity acts as a significant barrier to the adoption of autonomous systems.

The consensus in the medical community is that AI should function as a collaborative intelligence, augmenting the clinician's expertise rather than replacing it. The complexity and low prevalence of rare diseases necessitate human oversight to interpret contextual factors, manage patient expectations, and navigate the ethical landscape.

Navigating the Ethical Landscape

The ethical considerations surrounding AI in rare diseases extend beyond liability. Issues of data privacy, informed consent for data usage, and the risk of diagnostic exclusion (where AI models fail to recognize conditions not represented in their training data) are paramount. The goal is to ensure that AI democratizes access to diagnosis, not creates a new form of digital divide.

For more in-depth analysis on the ethical and technological challenges of integrating AI into complex medical fields, the resources at www.rasitdinc.com provide expert commentary. The future of rare disease care lies in a symbiotic relationship where AI handles the data complexity, and human clinicians provide the essential judgment, empathy, and accountability.

References

[1] Germain, D. P. (2025). Applying artificial intelligence to rare diseases. PubMed Central. [2] Wojtara, M. (2023). Artificial intelligence in rare disease diagnosis and treatment. Clinical and Translational Science. [3] Frías, M. (2025). The artificial intelligence challenge in rare disease diagnosis. Computers in Biology and Medicine. [4] Hallowell, N. (2023). Democratising or disrupting diagnosis? Ethical issues. eClinicalMedicine.

Conclusion

Can AI make decisions about rare diseases? The answer, currently, is no, not autonomously. AI is an unparalleled decision-support system that is revolutionizing the diagnostic phase, significantly reducing the diagnostic odyssey. However, the inherent challenges of data scarcity, the need for model explainability, and unresolved ethical and legal questions mean that the final, critical decision-making authority must remain with the human expert. The true breakthrough is not AI replacing the doctor, but AI empowering the doctor to deliver a diagnosis faster and with greater precision. This collaborative model is the most responsible and effective path forward for the millions affected by rare diseases.