Ateneo - UPO

The course addresses the principles of Evidence Based Medicine and critical evaluation of scientific literature, with a focus on their in biomedical research and data analysis.
The formulation of the research question, the hierarchy of evidence, the critical evaluation of the main study designs, the interpretation of the results of clinical trials, diagnostic studies, systematic reviews, and meta-analyses will be covered.
Part of the course will be dedicated to the evaluation of benefits and risks, uncertainty in the interpretation of evidence, and the main methodological aspects related to the use of predictive models and Artificial Intelligence systems in healthcare.

Greenhalgh T., Dijkstra P. How to Read a Paper: The Basics of Evidence-Based Healthcare. 7th Edition. Wiley-Blackwell, 2025.

The course aims to provide the tools needed to read and critically evaluate scientific literature and use available evidence to support research and decisions in the biomedical and healthcare fields.
Particular attention will be paid to the ability to evaluate the validity of studies, correctly interpret their results, distinguish between statistical significance and clinical relevance, and consider the benefits, risks, and applicability of evidence.
The course also aims to introduce the basic principles of decision analysis analysis and critical evaluation of AI-based decision support systems.

Basic knowledge of statistics and epidemiology is useful, particularly of the main study designs, measures of association, and confidence intervals.
The necessary concepts will still be recalled during the course.

The course includes lectures integrated with reading and discussion activities of scientific articles, abstracts, tables, graphs and clinical scenarios.
A significant part of the lessons will be dedicated to the practical application of critical appraisal criteria.

The teaching materials and articles used during the lessons will be made available to students on the DIR course platform.

The learning assessment will be aimed at assessing the student's ability to critically read and interpret scientific evidence.
The test may include closed-ended and open-ended questions, application exercises, and questions based on abstracts, tables, graphs, or short clinical scenarios.
In particular, the ability to critically read scientific literature, recognize the quality and robustness of available evidence, identify its main limitations, and evaluate its relevance, consistency, and applicability in the biomedical and healthcare fields will be evaluated. The ability to critically evaluate evidence supporting the use of AI models and systems will also be required, distinguishing between promising results and sufficiently robust, reproducible, and applicable evidence to support healthcare research and decisions.

1.Evidence Based Medicine and formulation of the research question
Principles of EBM and Evidence Based Healthcare. Formulation of the research question. PICO. Types of question. Sources and hierarchy of evidence.

2.Critical evaluation of scientific studies
Research question, population, exposure or intervention, comparator and outcome. Appropriateness of the study design. Internal and external validity. Bias. Accuracy of estimates and applicability of results.

3.Intervention studies
Randomized clinical trials. Randomization, control group, blinding and follow-up. Relative risk, absolute and relative risk reduction, Number Needed to Treat and Number Needed to Harm. Benefits, risks and clinical relevance.

4.Diagnosis, screening and clinical prediction
Sensitivity, specificity, predictive values, likelihood ratio, pre-test and post-test probabilities. Principles of evaluation of diagnostic studies and clinical prediction models. The text explicitly deals with both likelihood ratios and diagnostic and prognostic predictive models.

5.Systematic reviews and meta-analyses
Narrative review and systematic review. Selection of studies. Quality assessment. Principles of meta-analysis. Forest plot, heterogeneity and publication bias.

6.Guidelines and recommendations
Principles of guideline evaluation. Quality of evidence, balance between benefits and risks, and applicability of recommendations.

7.Decision analysis in the biomedical field
Decisions under uncertainty. Alternatives, probabilities and consequences. Benefits/Risks Balance Sheet. Introduction to decision trees, the concept of utility, and sensitivity analysis. Critical evaluation of studies using predictive models and AI systems in healthcare. Validation, generalizability, bias, safety, and integration in the biomedical context.

At the end of the course, the student will be able to formulate a research question, identify the most appropriate evidence, and critically evaluate its methodological quality and applicability.
It will also be able to interpret key measures of benefit, risk, and diagnostic accuracy, read the results of systematic reviews and meta-analyses, and use available evidence in simple decision-making processes.
The student will eventually acquire the basic tools to critically evaluate studies related to predictive models and Artificial Intelligence systems applied to biomedical research.