How Agentic AI Is Revolutionizing Rare Disease Identification Through Pattern Recognition

A New Chapter in Rare Disease Diagnosis

Rare diseases affect over 300 million people worldwide, but for many patients, getting an accurate diagnosis can take years. Traditional diagnostic workflows often rely on fragmented data, subjective evaluation, and trial-and-error treatments. That delay can mean worsening symptoms, misdirected therapies, and missed opportunities for intervention.

Agentic AI offers a new path forward. Unlike traditional AI tools that simply surface insights, agentic AI systems are designed to take intelligent action. By recognizing patterns across diverse datasets and orchestrating complex diagnostic tasks, they support earlier detection, more accurate classification, and faster initiation of treatment for rare diseases.

In this blog, we explore how agentic AI is helping pharma and medical teams close the diagnostic gap using real-world examples, including insights from Polestar LLP’s life sciences research.

What Is Agentic AI and Why It Matters in Diagnostics

Agentic AI refers to AI systems that not only analyze data but also take action autonomously within a defined set of rules and safety protocols. These systems are made up of multiple intelligent agents that can collaborate, learn from each other, and evolve over time.

In diagnostics, this means agentic AI can:

• Pull and synthesize data from disparate EHR (Electronic Health Record) systems
• Detect subtle symptom patterns and correlations in real-world patient data
• Flag anomalies for further clinical review
• Automate and optimize next-step workflows, such as specialist referrals or additional tests

This collaborative, action-oriented model makes agentic AI uniquely suited for the complexity of rare disease detection.

Real-World Example: Pattern Recognition in Rare Disease Detection

According to a Polestar LLP use case, agentic AI was deployed to tackle a key challenge: how to identify patients likely to have rare, underdiagnosed conditions.

The system used a network of agents to:

• Parse structured and unstructured patient records across provider networks
• Identify common misdiagnoses that often precede rare disease discovery
• Match patient history against known symptom clusters and biomarker profiles
• Trigger alerts to medical affairs and care teams when patterns emerged

"The agents learned to spot subtle trends in clinical language and lab results that physicians often overlook, enabling earlier intervention for conditions that previously went undetected for years."
Polestar LLP

This use of agentic AI not only improved identification but also reduced unnecessary tests and referrals by narrowing focus to high-likelihood patients.

Want to explore how agentic AI could support your rare disease initiatives? Schedule a custom CI Health AI Strategy Workshop and identify the right diagnostic workflows to pilot.

How Agentic AI Closes the Diagnostic Gap

Rare diseases often present with symptoms that mimic more common illnesses. This can lead to years of misdiagnosis and ineffective treatments. Agentic AI helps close this gap in four ways:

1. Pattern Recognition Across Silos
   Agentic systems don’t rely on a single data stream. They connect EHR, genomics, claims data, and more to form a comprehensive picture.
2. Multi-Agent Collaboration
   One agent may identify a biomarker pattern while another compares patient history against similar cases. Together, they orchestrate a clearer path to diagnosis.
3. Contextual Awareness
   These systems understand patient-specific factors like age, geography, and family history, adjusting predictions accordingly.
4. Action-Driven Intelligence
   Instead of just flagging a possible match, the system may recommend a targeted test, route the case to a rare disease specialist, or prepare clinical documentation.

This level of intelligence turns passive data into proactive diagnostic support.

Compliance, Transparency, and Trust

Because rare disease diagnostics often involve sensitive data and long-tail conditions, compliance and trust are paramount.

Agentic AI systems used in this context are typically built with:

• Audit Trails: Every action and recommendation is logged and traceable.
• Explainable AI Models: Agents can justify why a recommendation was made.
• Data Privacy Standards: Systems follow regulations like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation).
• Human Oversight: No action is taken without clinical validation or approval.

This ensures the technology augments, rather than replaces, clinician judgment while staying within regulatory bounds.

Opportunities for Pharma and Medical Affairs Teams

While many rare disease initiatives focus on patient engagement, agentic AI also creates new value for commercial and medical teams:

• Medical Affairs: Gain earlier insight into unmet patient needs and emerging treatment opportunities.
• Commercial Teams: Support market access strategies with data-backed prevalence insights.
• R&D Teams: Identify subpopulations for rare disease trials with greater speed and accuracy.
• HCP (Healthcare Provider) Support: Equip field teams with diagnostic support tools and education pathways.

These applications help pharma companies not only accelerate diagnosis but also drive stronger cross-functional alignment around patient outcomes.

Getting Started with Agentic AI for Rare Disease

If your organization is considering agentic AI for rare disease detection, here are practical next steps:

1. Identify Known Diagnostic Bottlenecks
   Where do patients typically fall through the cracks? Start there.
2. Audit Your Data Infrastructure
   Ensure systems can securely access and process diverse patient data.
3. Engage Clinical and Compliance Leaders Early
   Their input is critical for building usable and trusted AI workflows.
4. Pilot One Workflow
   Start small with a disease area you already understand well.
5. Measure Time-to-Diagnosis Improvements
   Focus on metrics like reduced referrals, higher diagnostic yield, and fewer unnecessary tests.

Conclusion: Rare Isn’t Invisible Anymore

Agentic AI is helping to rewrite the rare disease playbook. By recognizing subtle, overlooked patterns and automating follow-up workflows, it brings clarity and speed to an area long known for diagnostic delay.

With proper governance, cross-functional alignment, and pilot-ready use cases, pharma organizations can deploy agentic AI to close diagnostic gaps, improve patient outcomes, and advance the mission of precision medicine.

Want to explore how agentic AI could support your rare disease initiatives? Schedule a custom CI Health AI Strategy Workshop and identify the right diagnostic workflows to pilot.