Bernardo Neves is an internal medicine physician at Hospital da Luz Lisboa and clinical analytics lead of the Value Based Health Office at Luz Saúde, where he works on patient-reported outcomes, clinical indicators and population risk stratification.
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He holds a PhD in biomedical engineering from Instituto Superior Técnico, Universidade de Lisboa, on multimorbidity measurement and risk prediction from clinical data, and completed the Portugal Clinical Scholars Research Training programme at Harvard Medical School.
He teaches artificial intelligence in medicine at the Faculty of Medicine of the University of Lisbon and machine learning at Católica Medical School, and serves on the Artificial Intelligence Committee of the Ordem dos Médicos.
Current roles
- Head of Clinical Analytics and Value-Based Health
- Internal medicine physician
- Invited Professor of Artificial Intelligence in Medicine
- Invited Professor of Machine Learning and Research Data Management
- Supervisor of MSc theses in health data science
- Member, Artificial Intelligence Committee
- Member, Multimorbidity Working Group
Education
- PhD in Biomedical Engineering, Leveraging Data Science for Multimorbidity Measurement and Risk Prediction from Clinical Data
- Portugal Clinical Scholars Research Training
- Postgraduate degree in Business Analytics, Data Science and Big Data
- Residency in Internal Medicine
- MD
Research
- Principal investigator, IntelligentCare: outcome-centred decision support in multimorbidity
Selected publications
- AI assistance in medical decision-making: the role of recommendations and explanations in simulated clinical cases
- Multimorbidity measurement strategies for predicting hospital visits
- Large language models approach clinician performance in ESC cardiovascular risk stratification
- Workforce disruptions and the redefinition of clinical roles: artificial intelligence in healthcare
- Zero-shot learning for clinical phenotyping: comparing LLMs and rule-based methods
- Integrating human-centred AI in clinical practice
- Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis
- Impact of a wearable-based physical activity and sleep intervention in multimorbidity patients: trial protocol
- The quiet revolution of big data in medicine
Talks
- What healthcare teams ask for: reality, not hype, roundtable
- AI for risk stratification and complexity measurement in multimorbidity