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AI in clinical research: from hype to protocol
What machine learning and language models can and cannot do across the study lifecycle.
- Duration
- Four live sessions of 120 minutes over two weeks
- Format
- Live online (Zoom Webinar) with worked examples, maximum 30 participants per language cohort
- Who it is for
- Investigators, study coordinators, data scientists new to clinical research, and members of ethics committees.
Introduction from the faculty
A short faculty introduction will be added here.
A practical programme on what machine learning and language models can and cannot do in study design, recruitment, monitoring and analysis, and on how to validate, govern and document their use to the standard ethics committees and regulators expect.
Learning outcomes
- Distinguish validated AI applications from speculative ones
- Specify data, validation and governance requirements in a protocol
- Apply reporting guidelines for AI-enabled studies
- Use language models for evidence synthesis and study documentation with integrity
- Assess vendor claims critically
Curriculum
01Foundations without the jargon
- Supervised, unsupervised and generative models
- Data requirements and leakage
- Performance metrics that matter clinically
- Common failure modes
02AI across the study lifecycle
- Eligibility screening and recruitment
- Phenotyping and endpoint adjudication
- Monitoring and safety signals
- Analysis and prediction
03Validation, bias and governance
- Internal versus external validation
- Bias, fairness and subgroup performance
- Documentation and audit trails
- Data protection and residency
04Language models in research practice
- Evidence synthesis and screening
- Protocol and study documents drafted with disclosure
- Hallucination and citation checks
- Policies of funders, regulators and ethics committees
Sessions
Delivered via Zoom Webinar with EU data-residency settings.
English session
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Dates to be announced
Price announced with dates
Polish session
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Dates to be announced
Price announced with dates