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A scientist at a microscope

Life Sciences
Use Cases

Each part of a life sciences company has its own problems, data, and regulators. Start with your department.

Life Sciences overview

19 use cases

Search use casesAll departments

Clinical trials

2 use cases

Clinical trial and supply integrity monitoringAI clinical trial and supply integrity monitoring flags unusual patterns in site data, samples, and supply movements, with reasons for your teams to review.Clinical trial site selectionPredictive models that use past trial performance and feasibility data to estimate which sites are likely to enroll the patients a study needs, on time.

Commercial

5 use cases

Optimizing promotional spend and budget by channelAI models how each promotional channel, region and brand responds to spend, so your team can set the next budget from evidence, not last year's split.Personalized patient support programsAI for personalized patient support programs groups patients by shared needs and flags who may stop a therapy early, so support teams reach them in time.Pharma field force performanceAI agent performance scores pharma reps' opportunities and suggests next best actions from CRM and call data, cutting manual entry that keeps adoption low.Physician targeting for a product launchAI physician targeting for a product launch ranks the HCPs whose patients fit your newly approved therapy, so your teams reach the right prescribers first.Provider scoringLicensed prescribing and engagement data rank HCP accounts by fit to approved indications, so field and medical teams plan coverage from one explained score.

Research

4 use cases

Biomedical literature and knowledge graph synthesisAI biomedical literature synthesis reads publications and internal reports, maps genes and targets, and answers questions with a citation for each statement.Gene mutation risk scoringGene mutation AI scores genomic and phenotype profiles for genetic risk and ranks the genes and pathways that drive it, for research and clinical teams.Mechanism of action prediction for drug screeningMachine learning that estimates how a compound acts on biology from gene expression and cell viability data, so scientists can prioritize hits sooner.Patent landscape and competitive intelligenceA patent landscape and competitive intelligence assistant searches patents, trial registries and publications and answers questions with citations.

Safety

4 use cases

Biomedical literature and knowledge graph synthesisAI biomedical literature synthesis reads publications and internal reports, maps genes and targets, and answers questions with a citation for each statement.Classifying patient-reported drug reviewsAI reads what patients write about a drug and sorts each review as effective, ineffective or adverse, routing every possible adverse mention to a person.Drafting safety reports and submission sectionsAI drafts cited sections of safety reports and regulatory submissions from approved sources, and checks a package for gaps, for a qualified person to sign.Pharmacovigilance case intake and signal detectionAgents turn adverse event reports into structured cases, suggest coding terms for your coders to confirm, and surface possible safety signals in narratives.

Supply chain

5 use cases

Pharma inventory and out-of-stock risk predictionMachine learning on demand, inventory and order data that flags which pharma products are likely to run short and suggests reorders planners approve.Predictive maintenance for pharma manufacturingAI predictive maintenance for pharma manufacturing uses sensor, batch, and maintenance data to predict which equipment is likely to fail, and when.RFP and procurement automationAI RFP and procurement automation reads your RFP and every vendor proposal, compares them on scope, pricing and terms, and answers questions with citations.Supplier document validation against the ERPRead supplier contracts, purchase orders, invoices and certificates of analysis, check each value against your ERP, and send only mismatches to a person.Supplier risk scoringAn AI supplier risk model uses delivery, quality and commercial history to predict which pharma suppliers will likely miss a delivery or fail a quality check.

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