# Clinical Literature Synthesis with AI > Healthcare · AI Course · MED-10 > Source: https://ibl.ai/solutions/medical-healthcare/course/clinical-literature-synthesis-with-ai > Last updated: 2026-08-25 **Use AI for evidence review and research support in a clinical setting — search strategy, screening, extraction, and the verification that prevents a fabricated citation.** ## The Short Answer **AI accelerates evidence screening and extraction but fabricates citations, so every reference must be verified against the source. ibl.ai runs synthesis inside the organization where you own all the code and the data — necessary because pre-publication research and unblinded trial data cannot be sent to an external service.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Healthcare](https://ibl.ai/solutions/medical-healthcare) ## Course facts - **Level:** Intermediate - **Duration:** 5 hours across 8 modules - **Format:** Cohort workshop with synthesis labs - **Modules:** 8 - **Catalog code:** MED-10 - **Frameworks covered:** PRISMA, GRADE, HIPAA, Common Rule ## What is this course about? Evidence synthesis is reading at a volume clinicians do not have time for, which makes AI attractive and fabrication dangerous. This course covers systematic search strategy, title and abstract screening with human adjudication, data extraction and its accuracy problem, evidence grading, and mandatory citation verification. ## Who is this course for? - Clinical researchers and research staff - Medical librarians - Guideline development committees - Clinical educators ### What do I need before starting? - Research or evidence review experience - Familiarity with systematic review methodology helpful ## What will I be able to do afterwards? - Design a systematic search strategy with AI assistance - Screen titles and abstracts with human adjudication - Extract data and measure the accuracy problem honestly - Grade evidence quality appropriately - Verify every citation before it reaches a clinical audience ## What does each module cover? ### Module 1 — How do you build a search strategy? Systematic search where AI helps with term generation but not with coverage decisions. _(45 min)_ **Objectives** - Build a systematic search strategy - Use AI for term and synonym generation - Verify coverage against known included studies **Topics:** Search strategy · Term generation · Coverage verification · Database selection **Activity:** Build a search strategy and verify it retrieves known included studies. ### Module 2 — How do you screen at scale? Title and abstract screening with human adjudication and measured agreement. _(50 min)_ **Objectives** - Screen at scale with AI assistance - Maintain human adjudication - Measure agreement and calibrate **Topics:** Screening at scale · Human adjudication · Agreement measurement · Calibration **Activity:** Screen 200 abstracts and measure agreement against dual human screening. ### Module 3 — Why is data extraction the accuracy problem? Extraction from tables and results sections, where errors are frequent and consequential. _(55 min)_ **Objectives** - Extract structured data from studies - Measure extraction accuracy - Design verification proportional to consequence **Topics:** Data extraction · Table accuracy · Error measurement · Verification design **Activity:** Extract data from ten studies and verify every figure against the source. ### Module 4 — How do you grade evidence quality? Quality assessment and risk of bias, where AI assists but does not conclude. _(45 min)_ **Objectives** - Apply quality assessment frameworks - Use AI to surface assessment inputs - Keep the quality judgment human **Topics:** Quality frameworks · Risk of bias · AI assistance limits · Human judgment **Activity:** Assess quality for five studies with AI-surfaced inputs and human conclusions. ### Module 5 — How do you verify every citation? Citation verification, because a fabricated reference in clinical guidance is a safety issue. _(45 min)_ **Objectives** - Verify every citation exists - Verify the source supports the claim - Make verification non-optional **Topics:** Citation existence · Claim support · Verification workflow · Non-optional design **Activity:** Verify every citation in an AI-assisted synthesis for existence and claim support. ### Module 6 — How do you match patients to trials? Trial matching from eligibility criteria and the clinical record. _(45 min)_ **Objectives** - Match patients against trial eligibility criteria - Handle complex and ambiguous criteria - Keep enrollment decisions with the research team **Topics:** Eligibility matching · Criteria complexity · Ambiguity handling · Enrollment decisions **Activity:** Match a patient cohort against trial criteria and review the false matches. ### Module 7 — How do you keep pre-publication research inside? Protecting unpublished research and unblinded data from leaving the organization. _(40 min)_ **Objectives** - Classify research data by sensitivity - Keep pre-publication material in boundary - Verify no egress for sensitive workflows **Topics:** Research data classification · Pre-publication protection · Unblinded data · Egress verification **Activity:** Verify with network testing that a synthesis workflow does not egress research content. ### Module 8 — Building the synthesis workflow The lab module: a synthesis workflow with mandatory citation verification. _(50 min)_ **Objectives** - Build the workflow end to end - Enforce citation verification - Document methodology for publication **Topics:** Workflow build · Verification enforcement · Methodology documentation · Reproducibility **Activity:** Complete a synthesis through the workflow with full methodology documentation. ## What is the capstone project? **Evidence synthesis with verified citations.** Complete an evidence synthesis using the workflow: verified search strategy, screening with measured agreement, extraction with figure-level verification, human quality grading, mandatory citation verification, and documented methodology — with verified in-boundary processing. _Deliverable:_ A completed synthesis with methodology documentation and verification records. ## How are learners assessed? - Every citation verified for existence and claim support - Extraction accuracy measured with every figure checked against source - Screening agreement measured against dual human screening ## What ships with the course? - **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room. - **Learner workbook.** Exercises, checklists, and the templates each module's activity produces. - **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots. - **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome. - **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated. ## Which AI agents does this course use? - [Research Agent](https://ibl.ai/solutions/medical-healthcare/agent/research-agent) - [Clinical Support Agent](https://ibl.ai/solutions/medical-healthcare/agent/clinical-support-agent) - [Knowledge Management Agent](https://ibl.ai/solutions/medical-healthcare/agent/knowledge-management-agent) - [Quality Improvement Agent](https://ibl.ai/solutions/medical-healthcare/agent/quality-improvement-agent) ## Where does the course material come from? Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time. - [ClinicalTrials.gov](https://clinicaltrials.gov/) — NIH. Trial registry used for the matching module. - [National Institutes of Health](https://www.nih.gov/) — NIH. Research policy and data sharing requirements. - [Agency for Healthcare Research and Quality](https://www.ahrq.gov/) — AHRQ. Evidence synthesis methodology and quality assessment guidance. - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) — Lewis et al., arXiv. Technical basis for grounding synthesis in a real literature corpus. ## Delivery notes Binding guidance for anyone preparing and delivering this course: - Module 5 is a patient safety module, not an academic one. A fabricated citation in clinical guidance can change practice, and verification must be non-optional. - Module 3's extraction accuracy is systematically worse than participants expect, especially from tables. Measure it rather than asserting it. - Module 6's false matches are the teaching point. Trial matching over-matches by default and reviewing the false positives shows why the research team must decide. - Module 7's egress verification must be empirical. Pre-publication research leaving the organization is a career-level harm to the investigator. - Coordinate with MED-6 — evidence grounding for decision support draws on this workflow and the citation verification should be shared. ## Why run AI training on a platform you own? - **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data. - **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime. - **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount. - **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools. ## Frequently asked questions ### What does the Clinical Literature Synthesis with AI course cover? Evidence synthesis is reading at a volume clinicians do not have time for, which makes AI attractive and fabrication dangerous. This course covers systematic search strategy, title and abstract screening with human adjudication, data extraction and its accuracy problem, evidence grading, and mandatory citation verification. It runs 5 hours across 8 modules across 8 modules, at intermediate level, and closes with a capstone: Evidence synthesis with verified citations. ### Who should take Clinical Literature Synthesis with AI? It is written for Clinical researchers and research staff, Medical librarians, Guideline development committees, Clinical educators. Prerequisites: Research or evidence review experience; Familiarity with systematic review methodology helpful. ### Can we run this course on our own infrastructure? Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for healthcare teams that cannot send work to a public AI tool. ### How do we get access to Clinical Literature Synthesis with AI? Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter. ### How much does AI training for healthcare cost on ibl.ai? There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## More Healthcare courses - [HIPAA-Compliant AI: PHI, BAAs, and Where the Data Lives](https://ibl.ai/solutions/medical-healthcare/course/hipaa-compliant-ai): What HIPAA actually requires of an AI deployment — the BAA analysis, Security Rule safeguards, minimum necessary, and the architecture that keeps PHI inside your boundary. - [Clinical Documentation with AI: Ambient Notes and Review](https://ibl.ai/solutions/medical-healthcare/course/clinical-documentation-with-ai): Deploy ambient documentation safely — accuracy in clinical language, the attestation requirement, note bloat, and measuring whether it returns clinician time. - [Medical Coding with AI: ICD-10, CPT, and Denial Prevention](https://ibl.ai/solutions/medical-healthcare/course/medical-coding-with-ai): AI-assisted coding that improves accuracy rather than just speed — code suggestion, documentation gap detection, denial prevention, and staying clear of upcoding. - [Prior Authorization Automation with AI Agents](https://ibl.ai/solutions/medical-healthcare/course/prior-authorization-automation): Compress the highest-friction administrative process in healthcare — payer requirement lookup, packet assembly, status tracking, and appeal drafting. - [Patient Education Agents: Health Literacy and Safety](https://ibl.ai/solutions/medical-healthcare/course/patient-education-agents): Patient-facing AI that explains conditions and discharge instructions safely — reading level, language access, scope boundaries, and symptom escalation. - [AI in Clinical Decision Support: Evidence, Limits, and Liability](https://ibl.ai/solutions/medical-healthcare/course/ai-clinical-decision-support): Deploy clinical decision support responsibly — evidence grounding, automation bias, transparency obligations, and where liability actually lands.