Safety-relevant information hides across a growing stream of published articles and other sources, and screening it all by hand is slow and easy to fall behind on.
Pharmacovigilance obligations include monitoring the scientific literature — and increasingly other sources — for case reports and safety information about a company's products. The volume of published articles keeps growing, most of what is screened is not relevant, and missing a reportable case in the literature is a compliance risk. AI approaches help by searching, filtering, and prioritizing this stream, and by extracting candidate case details so specialists can focus on what actually matters.
How AI helps with literature monitoring in pharmacovigilance
Literature-monitoring tools apply natural language processing to the stream of published articles — and sometimes other sources — to filter out the large share that is irrelevant and surface the records that mention a company's products in a safety-relevant context. Beyond simple keyword matching, they can recognize product and event terms and their synonyms, cluster near-duplicate references, and rank what remains so reviewers see the most likely-relevant records first. Some tools also extract candidate case elements — product, event, patient, reporter — to speed the hand-off into case processing. The aim is to make an unbounded screening task manageable, not to decide what is reportable. The tool narrows and prioritizes; a qualified specialist still reads the source article, confirms relevance, decides whether it constitutes a valid case, and remains accountable for the reporting decision. Every flag should trace back to an article a person can open and verify.
What to evaluate before buying literature-monitoring AI
Recall is the property that matters most here: because the risk is missing a reportable case, ask how a tool balances catching everything relevant against the review burden of false positives, and how that balance can be tuned. Coverage is just as important — a tool only screens the sources it actually searches, so confirm which databases and other sources it draws on, how current they are, and how it handles the languages and terminology relevant to your products. Traceability and workflow fit round it out: every surfaced record should link to its source, extracted case elements should be reviewable rather than trusted blindly, and the tool should hand relevant hits into your case-processing workflow cleanly. Because this feeds a regulated obligation, examine validation, audit trails, and how screening decisions and their rationale are documented.
How teams typically get started
A practical entry point is running the tool over a period the team has already screened manually, then comparing what it flags against the records deemed relevant and the cases reported. That reveals whether it would have caught everything the team caught, how much extra noise it adds, and how well its prioritization matches expert judgment — all before it becomes the primary screen. Limiting the trial to one product or source keeps the comparison clean.
AI Use Cases That Address This Problem
Case Processing Automation
Signal Detection & Aggregate Reporting
Frequently asked questions
What does literature monitoring involve in pharmacovigilance?
Safety teams routinely screen the scientific literature — and increasingly other sources — for articles that describe adverse events or other safety information about their products, so any valid case can be captured and reported. The volume is large and mostly irrelevant, which makes thorough, timely screening a persistent operational burden.
How does AI help with literature screening?
It filters the incoming stream to remove the large share that is irrelevant, recognizes product and event terms and their synonyms, deduplicates references, and prioritizes the records most likely to matter — sometimes extracting candidate case details too. Specialists then read the flagged sources, confirm relevance, and make the reporting decisions.
Could an AI screen miss a reportable case in the literature?
Any screen can miss, which is why recall — catching everything relevant — is the property to scrutinize, and why a human stays in the loop. Ask how a tool balances completeness against noise, confirm it covers the sources and languages relevant to your products, and keep qualified specialists accountable for confirming relevance and reportability.
What should we ask a literature-monitoring vendor?
Ask which databases and sources the tool searches and how current they are, how it balances recall against false positives and whether that is tunable, whether every flagged record links to a verifiable source, how it handles languages and duplicates, how it hands cases into your workflow, and how validation and audit trails support your regulated obligation.
U.S. FDA went live with FAERS II adverse-event reporting powered by LifeSphere MultiVigilance
ArisGlobal announced the go-live of the U.S. FDA's Adverse Event Reporting System (FAERS II), an electronic safety-reporting platform powered by its LifeSphere MultiVigilance software — the federal drug-safety regulator running adverse-event intake on ArisGlobal technology as part of the FDA's technology modernization efforts.