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Data and Analytics
AI and Data & Analytics: Powering Tomorrow’s Intelligent Enterprise
Highlights
- Redacted medicolegal documents create unstructured data quality challenges by removing sensitive entity details, making it difficult to build accurate relationship triples for knowledge graphs.
- Agentic artificial intelligence (AI) can improve relationship extraction by combining semantic parsing, probabilistic reasoning, multi-agent orchestration, confidence scoring, and human-in-the-loop validation.
- Successful adoption depends on selecting the right use cases and applying strong governance controls to manage risks related to accuracy, explainability, privacy, compliance, and re-identification.
On this page
Data quality
In the real world, most medical and healthcare data is unstructured What’s more, poor quality strips much of its analytical value even before it reaches a model.
Sometimes, this is unavoidable because these issues are built into how the business operates. Redacted documents such as medicolegal case files are a good example. Personally identifiable information and other sensitive details, including doctor and patient information, institution names, drug or product information, manufacturer names, must be removed before the documents can be stored and reused.
This creates a complex challenge when building knowledge graphs, as relationships between entities must be identified by extracting information from redacted documents. The challenge compounds when forming relationship triples of subject, predicate, and object, because redaction can remove one or more parts of the triple. This leaves the relationship incomplete or impossible to reconstruct.
The fix
This is the kind of problem agentic artificial intelligence (AI) solves. Rather than treating redacted content as unusable, an agentic system can reconstruct relationships around the gaps.
To extract relationship triples, agentic AI combines semantic parsing of the visible text with probabilistic reasoning to infer missing entities. The process typically involves identifying anchor entities, parsing contextual predicates, and resolving redacted identifiers through anaphora resolution. For example, recognising that ‘redacted_patient_1’ was admitted in ‘Emergency’ or ‘redacted_doctor_1’ performed appendectomy. It may query supporting artifacts, domain-specific ontologies, or internal databases to resolve conflicts and enrich the extracted relationships.
Agentic AI
A multi-agent orchestration framework is highly effective in this context. A ‘planner agent’ assesses the document and determines a dynamic tool-selection strategy. An ‘extractor agent’ then parses the raw document, executes the planner’s specific instructions, replaces redacted entities with unique, trackable identifiers, and extracts relationship triples.
Meanwhile, an ‘evaluator agent’ validates the extracted triples, checks whether they comply with guardrails and quality criteria, and assign confidence scores. Finally, an ‘output agent’ exports the structured results in JSON format or loads them into a graph database. If the ‘evaluator agent’ assigns a low confidence score to a triple, the ‘output agent’ can prevent it from moving forward and route it to a human-in-the-loop queue for manual verification.
Right use cases
Agentic AI delivers the maximum value when applied selectively, to use cases like the one mentioned in the previous section, where complexity, ambiguity, and variability make traditional automation ineffective. Redacted medicolegal documents demand a system that can interpret incomplete information, infer relationships, validate confidence, and route uncertain outcomes for human review. By aligning agentic AI with these high-value, judgment-intensive use cases, organisations can improve data quality, strengthen governance, and accelerate downstream analytics.
Managing risks
The same inferential capability that makes agentic AI valuable here is also what makes it risky. Deploying it within any business process carries risks related to accuracy, explainability, privacy, and regulatory compliance. Since agentic AI may infer missing entities or relationships from incomplete information, low-confidence triples can produce misleading knowledge graphs.
There is also a risk of re-identification if redacted information is reconstructed beyond acceptable boundaries. Strong guardrails, confidence scoring, audit trails, and human oversight are, therefore, not optional extras. They are what makes responsible and compliant use of Agentic AI possible.