Next batch to be announced · Register your interest
AI in Clinical Research10-Day Program in CDM & Pharmacovigilance
Learn how AI, machine learning and NLP are changing clinical data management and drug safety. This 10-day live online bootcamp mixes core AI ideas with hands-on clinical use cases. No coding experience is needed.
- Format
- 10-day live online bootcamp
- Next batch
- To be announced
- Fee
- Your advisor shares the fee
- Certificate
- BCRI Certificate in AI in Clinical Research
- Coding needed?
- No — Python is taught from basics
Mention “AI bootcamp” in your question. We will contact you when dates are confirmed.
In short
What is AI in clinical research?
AI in clinical research means using machine learning and natural language processing to speed up routine work: sorting adverse event reports, suggesting MedDRA codes, finding duplicate cases, spotting data errors and drafting case narratives. Trained people still review every output. BCRI’s 10-day bootcamp teaches these uses in clinical data management and pharmacovigilance, with Python exercises and a capstone project.
How AI is used in clinical research today
AI does not replace drug safety or data teams. It handles repetitive steps so that people can focus on judgement. These are the main uses you will study:
Adverse event intake
Sorting incoming reports and flagging serious cases first.
MedDRA auto-coding
Suggesting codes for reported terms, which a coder then checks.
Duplicate detection
Finding reports that describe the same case from different sources.
Signal detection
Scoring unusual patterns in safety data for expert review.
Data cleaning
Spotting missing values, outliers and mismatches between EDC and lab data.
Narrative drafting
Drafting case narratives and summaries for human quality control.
Regulators are setting rules for this. The US FDA has published a discussion paper and draft guidance on AI in drug development, and the European Medicines Agency has adopted a reflection paper on AI across the medicine lifecycle. Both stress human oversight, validation and transparency.
The bootcamp
What the AI bootcamp covers
Core AI ideas, applied to real clinical data and drug safety work, with trainers from CROs and safety data teams.
Applied AI/ML for CDM & PV
Use supervised and unsupervised learning for AE triage, coding and signal detection.
NLP for safety narratives
Tokenisation, entity recognition, MedDRA auto-coding and narrative summaries with spaCy and Med7.
Real use-case practice
Duplicate case detection, data cleaning and reconciliation, with FAERS and EudraVigilance examples.
Tools and libraries
Python, pandas, scikit-learn, spaCy and the MedDRA browser, on public safety datasets.
Capstone and certificate
An end-to-end case study from AE intake to signals, a BCRI certificate, and resume and interview support.
Live with recordings
Live interactive sessions with 3-month access to recordings on the BCRI Class app.
Syllabus
10-day AI, ML and NLP syllabus for CDM and pharmacovigilance
From basics to a capstone project. Python is taught from the start, so you do not need coding experience.
Day 1 — AI, ML and NLP in healthcare
- What AI, ML and NLP are, with simple examples
- How they are changing healthcare
- Introduction to CDM and PV
- Use cases: chatbots for AE intake, predicting patient dropout
Day 2 — Clinical data and PV workflows
- Types of clinical data: CRFs, EDC, lab data, AE reports
- Data flow in CDM
- PV basics: SAEs and ICSRs
- MedDRA and WHO-DD; from SAE to regulatory report
Day 3 — Machine learning basics
- Supervised vs unsupervised learning
- Classification, regression and clustering
- Training vs testing; overfitting
- Examples: serious vs non-serious AEs, dropout risk
Day 4 — NLP in PV and CDM
- Tokenisation, lemmatisation and named entity recognition
- Extracting AEs, drugs and doses from text
- Tools: spaCy, NLTK, Med7, cTAKES
- Parsing AE narratives; auto-coding with MedDRA
Day 5 — Automating medical coding
- What medical coding is and why it matters
- How AI maps reported terms to MedDRA
- NLP and ML classifiers for coding
- Example: “dizzy spell” to a MedDRA LLT
Day 6 — Duplicate and signal detection
- Duplicate detection with similarity measures and embeddings
- Signal detection: disproportionality analysis and ML scoring
- How FAERS and EudraVigilance data are used
- Example: flagging a rare AE trend
Day 7 — Data cleaning and reconciliation
- Missing values, outliers and inconsistencies
- ML for anomaly detection
- Rule-based vs ML-based data checks
- Example: reconciling EDC and lab data
Day 8 — AE narratives with NLP
- Basics of narrative writing
- Large language models for draft narratives
- Human-AI collaboration for quality control
- Example: a draft narrative from an ICSR
Day 9 — Industry tools and platforms
- SAS, Python and the MedDRA browser
- How safety systems use automation
- Public PV data sources such as VigiAccess
- Python libraries: pandas, spaCy, scikit-learn
Day 10 — Capstone and career guidance
- Case study: AE intake → coding → signal detection
- Where AI fits in the PV and CDM pipeline
- Career roles and next steps
- Resources for further learning
Careers
Careers that use AI in clinical data and drug safety
- AI-enabled Pharmacovigilance Analyst
- Clinical Data Scientist
- Safety Data Analyst
- PV Automation Specialist
- Clinical Data Manager with AI skills
- Healthcare NLP Associate
Where these roles are
- Contract research organisations (CROs)
- Pharma and biotech companies
- Drug safety units
- Healthcare AI and analytics firms
- Life-science technology companies
AI skills add to your clinical research foundation — they do not replace it. Employers look for people who understand both the science and the tools. Pay varies widely by role and employer, so we do not quote AI salary figures.
Certificate
On completion you receive a BCRI certificate for the programme, plus resume and mock-interview support.
Start building the foundation now
AI work builds on CDM and drug safety knowledge. These courses run every month:
- Clinical Data Management Training →2 months · EDC practice · online or Bangalore
- Pharmacovigilance Training →2 months · safety database practice · online or Bangalore
- Clinical SAS Course →3 months · live online · programming for trial data
- PG Diploma in Clinical Research →7.5 months · CR, CDM, PV and SAS · internship
FAQs
AI in clinical research course FAQs
What is AI in clinical research?
It is the use of machine learning and natural language processing to support clinical research work — for example sorting adverse event reports, suggesting MedDRA codes, finding duplicate cases, cleaning trial data and drafting narratives. People still review and approve the results.
When does the next batch start?
The next batch is not scheduled yet. Register your interest with the form on this page and an advisor will contact you when dates are confirmed.
Who should join this bootcamp?
CDM and pharmacovigilance professionals who want to add AI skills, and life science, pharmacy or medical graduates who want a future-ready start in clinical research.
Do I need coding knowledge?
No. Python is taught from the basics. Knowing clinical research terms such as CDM, PV, AE and MedDRA helps.
How is the programme delivered?
It is a 10-day live online bootcamp with assignments, case studies and a capstone project. Recordings are available for 3 months on the BCRI Class app.
What will I learn?
AI, ML and NLP basics for healthcare; CDM and PV workflows; MedDRA auto-coding; duplicate and signal detection; data cleaning; AI-assisted narrative drafting; and tools such as Python, pandas, spaCy and scikit-learn.
What certificate will I get?
You receive a BCRI Certificate in AI in Clinical Research.
What does the course cost?
Your advisor shares the fee when the next batch is announced.
What careers can this lead to?
Roles such as AI-enabled pharmacovigilance analyst, safety data analyst, clinical data scientist and PV automation specialist — usually after building a foundation in CDM or pharmacovigilance.
Can I start with another course while I wait?
Yes. Many learners start with clinical data management or pharmacovigilance training, which gives the domain knowledge that AI work builds on.