AI use cases in life sciences: 12 systems pharma and biotech build

The AI use cases pharma and biotech companies build first are literature and evidence mining, protocol drafting and feasibility, clinical data review, safety case intake and promotional material pre-checks for MLR review. They work because they speed up documents and reviews that already have an accountable human owner, and they hold up when built as GxP-validated systems with Part 11-ready records from the start.

Here are twelve systems for pharma, biotech, medical device and CRO teams, organized by research, clinical development, regulatory and safety, and commercial and manufacturing quality. Each lists the source data, what it integrates with, the hard parts and an illustrative team. Ryz serves enterprises across industries, including life sciences. Our AI pod teams build in your cloud and repos, working on your team with your clinical, regulatory, quality and IT staff. For provider and payer systems, see the separate healthcare use cases guide.

How the budgets work. Teams and timelines are illustrative planning assumptions, not quotes, and validation effort varies widely by system risk. Costs come only from Ryz's public rates: mid-level $7,000 to $10,000, senior $10,000 to $15,000 and tech lead $15,000 and up per engineer per month, quoted per team, using the $15,000 lead floor.

GxP and FDA expectations

• Electronic records. 21 CFR Part 11 sets requirements for electronic records and signatures used to meet FDA predicate rules: validation, audit trails, access controls and signature controls. Any AI system that writes into a GxP record needs them.
• Good clinical practice. ICH E6(R3), finalized by ICH in January 2025 and published by FDA as final guidance in September 2025, emphasizes risk-based quality management and fitness for purpose of computerized systems in trials.
• AI in regulatory decisions. FDA's January 2025 draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, proposes a risk-based credibility assessment tied to each model's context of use.
• Safety reporting and promotion. Postmarketing reporting under 21 CFR 314.80 requires serious and unexpected adverse experiences to be reported within 15 calendar days, and prescription drug advertising must meet 21 CFR 202.1 on fair balance and substantiation.

Research

1. Literature and evidence mining

Scientists ask which targets have published genetic evidence for a disease, or what safety signals have been reported for a mechanism. The system searches PubMed, preprints, patents and internal reports, extracts the relevant findings into a table and cites each source paragraph.

• Source data: PubMed and PMC, licensed journal content, patents, internal study reports.
• Integrates with: knowledge graphs, internal search, document management.
• Hard parts: entity resolution for genes, compounds and diseases with many synonyms, and licensing limits on full text. See knowledge graphs.
• Illustrative team: tech lead plus two senior engineers, three to five months: $35,000 to $45,000 a month, $105,000 to $225,000 in total.

2. Assay data analysis and property prediction

Models trained on a company's own assay results predict properties such as potency, solubility or stability for new candidates, so chemists and biologists prioritize which ones to make and test next. Scientists decide; the model ranks.

• Source data: assay results, compound or sequence registries, experimental metadata.
• Integrates with: ELN and LIMS (Benchling, LabWare, IDBS), registration systems, HPC or cloud GPUs.
• Hard parts: inconsistent assay protocols over the years and data that is sparse per target. Clean metadata matters more than model choice. See machine learning development.
• Illustrative team: tech lead plus three senior engineers, five to seven months: $45,000 to $60,000 a month, $225,000 to $420,000 in total.

3. Lab notebook structuring

Free-text ELN entries are converted into structured records: materials, lot numbers, conditions, results and deviations, so the data can be searched and reused across programs.

• Source data: ELN entries, instrument outputs, controlled vocabularies.
• Integrates with: ELN, LIMS, the scientific data platform.
• Hard parts: each lab's shorthand, and the rule that the original entry stays the record of truth.
• Illustrative team: tech lead plus one senior and one mid-level engineer, three to four months: $32,000 to $40,000 a month, $96,000 to $160,000 in total.

Clinical development

4. Protocol drafting and feasibility

The system drafts protocol sections from the company's templates and prior protocols, and tests draft inclusion and exclusion criteria against real-world data to estimate how many patients would qualify, so the team can see which criteria shrink the eligible pool most.

• Source data: protocol templates, past protocols and amendments, licensed real-world data, ClinicalTrials.gov records.
• Integrates with: authoring tools, statistical environments, document management (Veeva Vault).
• Hard parts: translating criteria into queries on coded data, and real-world data that underrepresents some populations.
• Illustrative team: tech lead plus three senior engineers, four to six months: $45,000 to $60,000 a month, $180,000 to $360,000 in total.

5. Site selection and enrollment forecasting

Models rank candidate sites by expected enrollment, start-up speed and data quality from past trial performance, and forecast enrollment curves, so clinical operations can add sites before a study slips.

• Source data: historical site performance, CTMS data, investigator databases, epidemiology by region.
• Integrates with: CTMS (Veeva Vault CTMS), feasibility surveys, planning dashboards.
• Hard parts: each sponsor sees only its own history. CRO and licensed data can fill gaps if contracts allow.
• Illustrative team: tech lead plus two senior engineers, three to five months: $35,000 to $45,000 a month, $105,000 to $225,000 in total.

6. Clinical data review and query drafting

The system reviews incoming trial data for inconsistencies (a lab value out of range without an adverse event, a dosing date after a visit), drafts data queries for sites and highlights patterns for medical monitors.

• Source data: EDC data, lab feeds, data review plans, past queries.
• Integrates with: EDC (Medidata Rave, Veeva EDC), clinical data repositories, CDISC SDTM pipelines.
• Hard parts: queries must be accurate and courteous, because sites are busy. Data managers issue every query, and the system's role belongs in the risk-based quality plan.
• Illustrative team: tech lead plus three senior engineers, five to six months: $45,000 to $60,000 a month, $225,000 to $360,000 in total.

7. Trial master file classification and QC

Thousands of TMF documents arrive from sites and vendors. The system classifies each to the TMF Reference Model, extracts metadata such as site, date and version, checks signatures and completeness, and queues exceptions for the TMF team.

• Source data: TMF documents, the reference model, past classification decisions.
• Integrates with: eTMF (Veeva Vault eTMF), site portals.
• Hard parts: scanned documents with handwritten signatures, and inspection readiness. Every automated filing must be traceable. See intelligent document processing.
• Illustrative team: tech lead plus two senior engineers, three to four months: $35,000 to $45,000 a month, $105,000 to $180,000 in total.

Regulatory and safety

8. Regulatory document drafting

Medical writers get first drafts of clinical study report sections, eCTD Module 2 summaries and responses to health authority questions, assembled from tables, listings, prior submissions and approved language, with every statement linked to its source.

• Source data: tables, figures and listings, prior submissions, style guides, health authority correspondence.
• Integrates with: regulatory information management (Veeva Vault RIM), authoring and publishing tools.
• Hard parts: numbers must match the tables exactly, so they are inserted from data, not generated. Writers own the text. See generative AI development.
• Illustrative team: tech lead plus three senior engineers, five to seven months: $45,000 to $60,000 a month, $225,000 to $420,000 in total.

9. Adverse event case intake

Safety teams receive reports by email, call center notes, literature, partners and social channels. The system detects adverse events, extracts the minimum case criteria, codes terms to MedDRA, flags seriousness and expectedness for review, and creates the case in the safety database with the clock started.

• Source data: source documents, past processed cases, MedDRA and product labeling.
• Integrates with: safety databases (Oracle Argus, ArisGlobal LifeSphere, Veeva Vault Safety), medical information and call center systems.
• Hard parts: missing an adverse event is a compliance failure, so recall is tuned high and safety physicians confirm seriousness and causality. Expedited reports still run on the 15-day clock.
• Illustrative team: tech lead plus four senior engineers, six to eight months: $55,000 to $75,000 a month, $330,000 to $600,000 in total.

10. Medical information response drafting

Medical information specialists answer unsolicited questions from clinicians about dosing, interactions or off-label data. The system finds approved standard response documents and evidence, drafts a reply and flags any part that would need a new standard response.

• Source data: standard response letters, labeling, published evidence, inquiry history.
• Integrates with: medical information CRM, content management (Veeva Vault MedComms), contact center.
• Hard parts: keeping replies within approved content, and capturing any adverse event mentioned in an inquiry for safety intake. See RAG development.
• Illustrative team: tech lead plus two senior engineers, three to four months: $35,000 to $45,000 a month, $105,000 to $180,000 in total.

Commercial and manufacturing quality

11. Promotional material pre-check for MLR review

Before a piece goes to medical, legal and regulatory review, the system checks every claim against approved references and labeling, checks fair balance and required safety information, and lists issues for the submitter to fix. A Ryz AI pod built marketing compliance review for a leading alternative asset manager that handles 8,000+ documents and cut review time from days to hours (case study); claim-to-reference checking in pharma follows a similar pattern under different rules.

• Source data: promotional pieces, approved claims library, references, labeling, past MLR comments.
• Integrates with: promotional review systems (Veeva Vault PromoMats), digital asset management.
• Hard parts: fair balance depends on layout and prominence as well as words. MLR reviewers keep approval authority.
• Illustrative team: tech lead plus three senior engineers, four to six months: $45,000 to $60,000 a month, $180,000 to $360,000 in total.

12. Batch record review by exception

For GMP manufacturing, the system reviews executed electronic batch records and attached data, confirms each step against the master record, flags exceptions, alarms and late entries, and lets QA reviewers focus on the exceptions before release.

• Source data: executed batch records, master batch records, process data, deviation history.
• Integrates with: MES and eBR systems, LIMS, quality systems (Veeva Vault QMS, MasterControl).
• Hard parts: this sits inside a validated GMP process, so the review logic needs full validation and Part 11 controls, and QA keeps release authority.
• Illustrative team: tech lead plus two senior engineers and one mid-level engineer, five to seven months: $42,000 to $55,000 a month, $210,000 to $385,000 in total.

How to pick your first use case

• Start outside the validated core. Literature mining, protocol drafting and feasibility can show value without full GxP validation, while you build the validation approach for later systems.
• Set context of use and risk early. For each system, write down what decision it informs and what happens if it is wrong. That drives validation depth and matches FDA's draft credibility framework.
• Go where review queues are longest. Safety intake, TMF QC and MLR pre-checks have measurable backlogs and cycle times.
• Keep source traceability everywhere. Inspectors and reviewers ask where a statement came from. Systems that cite sources survive audits.
• Assess readiness. Our AI readiness scorecard covers data access and governance in about ten minutes.

What it costs

Total cost is engineers times months times monthly rate. Ryz rates: mid-level $7,000 to $10,000, senior $10,000 to $15,000 and tech leads $15,000 and up per month, quoted per team.

• Research or TMF pilot (tech lead plus two senior engineers) for four months: $35,000 to $45,000 a month, $140,000 to $180,000 in total.
• Safety intake pod (tech lead plus four senior engineers) for eight months: $55,000 to $75,000 a month, $440,000 to $600,000.
• Full AI pod of about seven senior engineers including a tech lead and an ML engineer: $75,000 to $105,000 a month.

Validation documentation can add meaningful time to GxP systems; plan it into the timeline rather than after it. Cloud, licensed data and model usage run in your accounts and are not included; see AI development cost. You meet the named engineers before work begins, and a team can usually start in under two weeks once you say go. If you want a validated, vendor-supported module inside your existing Vault or safety platform with no custom work, license it from that vendor; Ryz pods build the integrations and custom systems around it.

Related

• Life sciences engineering teams
• Hire machine learning engineers
• LLM evaluation
• AI agents for compliance
• AI use cases in legal services
• AI use cases in manufacturing

FAQ

What are the main AI use cases in pharma and life sciences?

Literature mining, assay-based property prediction, protocol drafting and feasibility, site selection, clinical data review, TMF QC, regulatory writing, adverse event intake, medical information responses, MLR pre-checks and batch record review are the systems companies most often take to production.

Does AI in clinical trials need to be validated?

Computerized systems that affect trial data, participant safety or GxP records need validation proportionate to risk under ICH E6(R3) and Part 11. Research tools that do not create GxP records usually need lighter controls, documented in your quality approach.

What does FDA's draft AI guidance ask sponsors to do?

It proposes a risk-based credibility framework: define the question and the model's context of use, assess model risk, then plan, run and document credibility activities in proportion to that risk, and engage FDA early for high-risk uses. It is draft guidance and not yet final.

Can AI process adverse event reports?

Yes, for intake, extraction and coding, with safety professionals confirming seriousness, expectedness and causality. The system must be validated, tuned to avoid missed events and keep regulatory clocks visible.

Can a Ryz pod work with Veeva, Medidata and Argus?

Yes. Pods build integrations and custom systems against these platforms through their APIs and integration options, in your cloud and repos, inside your validation framework. You control access and own all code and IP.

Questions we didn't answer? Email info@ryzlabs.com.