Agentic AI for ICSR Detection: Enhancing Accuracy and Reducing False Positives

In the world of pharmacovigilance, accurate detection of Individual Case Safety Reports (ICSRs) is essential for safeguarding public health. ICSRs form the cornerstone of post-marketing surveillance, helping pharmaceutical companies and regulators detect adverse drug reactions (ADRs) and make informed decisions. However, the sheer volume of incoming data from diverse sources poses a significant challenge—leading to missed cases, delayed responses, or overwhelming false positives that burden safety teams.

Agentic AI—a new breed of intelligent, context-aware, and autonomous systems—is redefining the landscape of ICSR detection. By mimicking human reasoning while scaling far beyond human capability, agentic AI enhances both the accuracy and efficiency of identifying valid ICSRs in real time. This blog explores how agentic AI enables high-precision detection, reduces false positives, and transforms pharmacovigilance workflows.

Understanding ICSR Detection and Its Challenges

An Individual Case Safety Report (ICSR) is a report containing details of an adverse event experienced by a patient, suspected to be linked to a drug. These reports include crucial data points like patient demographics, suspected drugs, reactions, outcomes, and reporter information.

Challenges in Traditional ICSR Detection

1. Unstructured and Noisy DataICSRs can originate from various channels: emails, call center logs, social media, EHRs, scientific literature, and regulatory portals. Much of this data is unstructured and requires intelligent parsing.

2. Volume and VelocityWith thousands of data points flowing in daily, manual review processes struggle to keep pace, leading to missed reports or backlogs.

3. High False Positive RateKeyword-based filters or rule-based detection systems often flag irrelevant content, resulting in a high rate of false positives and increased verification workload.

4. Variability in ReportingInconsistencies in how adverse events are reported—due to cultural, linguistic, or medical terminology differences—further complicate detection.

What Is Agentic AI?

Agentic AI refers to AI systems that demonstrate goal-directed, autonomous behavior—much like a software “agent” that can think, learn, and act. These systems are context-aware, continuously learning, and capable of interacting with complex environments to achieve defined outcomes.

Key Features:

• Autonomy: Performs tasks independently without constant human oversight.

• Contextual Understanding: Processes nuanced inputs beyond surface-level keywords.

• Adaptive Learning: Learns from historical data and human feedback to improve over time.

• Goal-Oriented Reasoning: Identifies ICSRs with a focus on regulatory completeness and relevance.

These capabilities make agentic AI particularly well-suited to the ambiguous and dynamic nature of ICSR detection.

How Agentic AI Enhances ICSR Detection

1. Advanced Natural Language Processing (NLP)

Agentic AI uses cutting-edge NLP techniques such as transformer models (e.g., BERT, GPT, RoBERTa) to analyze unstructured text. It can:

• Extract named entities like drug names, adverse reactions, patient age/gender, and outcomes.

• Detect nuanced language that implies causality (e.g., “after taking X, patient experienced…”).

• Understand negations and context (e.g., “no side effects observed” vs. “severe headache reported”).

2. Intelligent Pattern Recognition

Unlike traditional rule-based systems, agentic AI identifies complex patterns in how ADRs are reported. It can recognize:

• Co-occurrence of symptoms and drug mentions.

• Temporal relationships (e.g., timing of symptom onset).

• Source credibility and structuredness (e.g., physician vs. social media report).

3. Multimodal Data Processing

Agentic AI can ingest and analyze information across multiple formats—text, audio (from call centers), PDFs (literature reports), and structured XMLs (regulatory data). This allows for more comprehensive coverage of potential ICSRs.

4. Precision Filtering and Deduplication

Through continuous learning, agentic AI reduces noise by accurately distinguishing between:

• True ICSRs that meet regulatory criteria.

• Incomplete or irrelevant cases that need follow-up or rejection.

• Duplicate reports across different sources.

5. Scoring and Classification Models

Each potential case is assigned a confidence score based on predefined criteria (completeness, seriousness, source, etc.). The AI then:

• Flags high-confidence cases for auto-submission.

• Queues borderline cases for human review.

• Logs low-confidence reports for passive monitoring.

Reducing False Positives: A Game-Changer

False positives are one of the most significant productivity drains in pharmacovigilance. Every false alert requires manual review, introduces delay, and adds cognitive burden to safety reviewers.

How Agentic AI Minimizes False Positives:

• Semantic Understanding: Goes beyond surface text to interpret medical context.

• Continuous Feedback Loops: Learns from cases accepted or rejected by safety reviewers.

• Source Profiling: Applies stricter thresholds to unverified sources and relaxes them for trusted ones.

• Historical Cross-Referencing: Compares with known product profiles and historical ICSRs for validation.

As a result, teams spend less time verifying irrelevant cases and more time acting on genuine safety signals.

Real-World Examples

1. Global Pharma Company

A top-10 pharma firm implemented agentic AI for ICSR detection across its call center and email channels. The result?

• 50% reduction in false positives

• 70% faster case detection time

• 30% fewer missed cases compared to manual review

2. Social Media Surveillance

Using agentic AI, a life sciences company monitored Twitter and Facebook for product-related adverse events. The system could filter out chatter and detect valid reports with 85% accuracy, flagging cases that manual teams had missed entirely.

Human-in-the-Loop (HITL): Enhancing Oversight

Despite its power, agentic AI works best with human oversight. Safety reviewers remain essential for:

• Resolving ambiguous or novel cases.

• Overriding false negatives.

• Auditing AI decisions for regulatory compliance.

The feedback loop between AI and humans ensures continuous model refinement and builds trust across the pharmacovigilance team.

Regulatory Compliance and Transparency

Key Considerations:

• Explainability: Agentic AI systems are designed with auditability in mind—providing rationales for why a case was flagged or rejected.

• Data Privacy: Adheres to GDPR, HIPAA, and other global standards through encryption, anonymization, and access controls.

• Validation and Audit Trails: Logs every decision, enabling backtracking for inspections or audits.

Regulators like the EMA and FDA are increasingly open to AI-driven workflows, provided transparency and accountability are maintained.

Future Outlook

Agentic AI is evolving rapidly. In the future, we can expect:

• Predictive Safety Monitoring: AI will not just detect ICSRs but predict ADR trends before they emerge.

• Federated Learning: Multiple organizations can train AI collaboratively without sharing sensitive data.

• Multilingual, Global ICSR Detection: Systems will handle adverse event reports across languages and regions with equal fluency.

Conclusion

Agentic AI is a powerful ally in the ongoing mission to protect patients and ensure drug safety. Its ability to detect ICSRs with high precision, eliminate false positives, and streamline pharmacovigilance workflows is revolutionizing the industry.

By integrating advanced NLP, learning algorithms, and contextual reasoning, agentic AI doesn't just automate ICSR detection—it amplifies it. As the technology matures and regulatory frameworks adapt, we can look forward to a future where adverse events are spotted earlier, investigated faster, and acted upon more effectively—saving both lives and resources.