Find safety signals earlier

Detect adverse event signals in literature

Safety NLP / LLMs Pilot Indirect

Natural language processing scans scientific literature and case reports to identify potential adverse-event information for safety review.

Discuss this use case Full evaluation

Medical information specialist assessing scientific evidence

(01) The opportunity

Why this use case matters.

Manual literature monitoring is time-intensive and can make it difficult to identify relevant safety information consistently at scale.

Faster review cycles, more consistent monitoring, and earlier attention to potential safety issues.

Primary user: Drug Safety Scientist
Business process: Literature monitoring and signal detection
Primary technology: Natural Language Processing / Large Language Models
Human responsibility: Review every candidate, determine reportability, and escalate through established safety processes

(02) How it works

From data to a human decision.

The technology supports a defined workflow. It does not replace the accountable professional.

Inputs and data

Scientific articles, case reports, and approved safety sources

AI capability

Classifies articles and extracts passages that may contain reportable safety information

Output and action

Prioritized articles and candidate signals for assessment

(03) Use case evaluation

Value versus effort.

This indicative view balances potential business value with the effort required to implement and operate the use case.

Quick winsHigh value, low effortPrioritize and execute

Strategic betsHigh value, high effortPlan and invest

Nice to haveLow value, low effortConsider when capacity allows

AvoidLow value, high effortDeprioritize

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