AI for Health & Research Lab

Where Artificial Intelligence
Meets Human Health

Generating rigorous, up-to-date evidence on the impacts of AI across health systems and research ecosystems.

Focus

Two Pillars

// Stream 01

AI Impacts on Human Health

We conduct systematic reviews evaluating how artificial intelligence technologies affect patient experiences, outcomes, clinical decision-making, and population health across diverse healthcare settings.

// Stream 02

AI for Research Efficiency and Quality & Quality

We examine how AI tools can transform the research pipeline — from literature synthesis to peer review — assessing impacts on efficiency, rigor, reproducibility, and the overall quality of scientific output.

Who We Are

Rigorous Evidence
for an AI-Driven
World

The AI for Health and Research Lab is dedicated to producing high-quality, systematic evidence on two transformative questions of our time: how is artificial intelligence reshaping human health, and how is it changing the way science is done?

We live in a moment of rapid, often uncritical adoption of AI across healthcare and research. Our mission is to cut through the hype with rigorous methodology — applying systematic review techniques, meta-analytic frameworks, and quality improvement principles to build a reliable evidence base.

Our team is conducting a series of systematic reviews examining the real-world impacts of AI on patient experiences, patient outcomes, clinical workflows, and health equity. In parallel, we are studying how AI tools can benefit the research process.

// 01 — Health

AI Impacts on Human Health

Evaluating AI diagnostic tools, predictive models, clinical decision support systems, and care delivery technologies through the lens of patient safety, equity, and outcomes.

// 02 — Research Efficiency

AI for Research Efficiency

Assessing how AI accelerates the research lifecycle — from hypothesis generation to systematic review automation — while preserving scientific rigor.

The People

Our Team

A multidisciplinary group at the intersection of medicine, data science, and evidence synthesis.

Niklas Bobrovitz
Research & QI Lead
Niklas Bobrovitz - MD, MSc, DPhil
University of Calgary

Leading the lab's systematic review program and quality improvement initiatives, Niklas brings expertise in evidence synthesis and health systems research to drive rigorous, impactful science.

Rohit Arora
AI Lead
Rohit Arora - PhD Student
Harvard University

Rohit leads the lab's artificial intelligence strategy, bringing deep technical expertise in machine learning and AI applications to guide the lab's research agenda and methodological innovation.

Research Output

Key Projects & Publications

Here are a list of key AI-focused research and quality improvement projects and articles that members of our team have contributed to.

01
Development of Prompt Templates for Large Language Model-Driven Screening in Systematic Reviews

Diagnostic test accuracy study to develop and validate generic prompt templates for large language model (LLM)-driven abstract and full-text screening that can be adapted to different reviews..

Annals of Internal Medicine. 2025.
02
A real-world evaluation of the implementation of NLP technology in abstract screening of a systematic review.

A quality improvement project evaluating the implementation of natural language processing to expedite systematic review screening.

Research Synthesis Methods. 2023.
03
AI-driven protein design.

A review exploring how AI-driven protein design is being used to engineer therapeutic proteins and create novel biomolecular systems with applications in synthetic biology, drug development, and biotechnology.

Nature Reviews Bioengineering. 2025.
04
Artificial intelligence-based clinical decision support in the emergency department: A scoping review.

A scoping review mapping the evidence on AI clinical decision support tools for the emergency department.

Academic Emergency Medicine. 2025.
05
Use of ambient AI scribes in the ED: what are we waiting for?

Editorial discussing the current evidence for the use of AI scribes in emergency departments.

Canadian Journal of Emergency Medicine. 2026.