STUDENT-LED RESEARCH LAB — UNIVERSITY OF TORONTO

INQUIRO

Reading the signal of AI in medicine.
DOMAINS00
RESEARCHERS00
INSTITUTIONU OF T
STATUSACTIVE

We analyze AI processing models and their applications across clinical decision-making, radiology, surgery, medical education, and risk stratification — working in collaboration with various doctors and researchers across the field.

DEEP LEARNING · CHEST CT · SEGMENTATION · DIAGNOSTIC REASONING · MRI RECONSTRUCTION · FOUNDATION MODELS · DIGITAL PATHOLOGY · RISK PREDICTION · LLMs IN THE CLINIC · ALGORITHMIC EQUITY · DEEP LEARNING · CHEST CT · SEGMENTATION · DIAGNOSTIC REASONING · MRI RECONSTRUCTION · FOUNDATION MODELS · DIGITAL PATHOLOGY · RISK PREDICTION · LLMs IN THE CLINIC · ALGORITHMIC EQUITY · DEEP LEARNING · CHEST CT · SEGMENTATION · DIAGNOSTIC REASONING · MRI RECONSTRUCTION · FOUNDATION MODELS · DIGITAL PATHOLOGY · RISK PREDICTION · LLMs IN THE CLINIC · ALGORITHMIC EQUITY ·
                    

A lab that reads the machines that read us

Inquiro is a student-led research lab focused on analyzing the use of artificial intelligence in medicine. We review current literature, evaluate AI processing models, and critically examine their potential applications in clinical decision-making, radiology, surgery, medical education, and risk stratification — working in collaboration with various doctors, clinician-scientists, and researchers across these fields. Our work is rooted in rigorous evidence synthesis, interdisciplinary thinking, and a shared conviction that understanding AI's capabilities and limitations is essential for the next generation of physicians.

METHOD.LOG
▸ review current literature across six clinical AI domains
▸ evaluate AI processing models — proprietary and open-source
▸ synthesize evidence on accuracy, safety, and clinical utility
▸ collaborate with physicians and researchers in the field
▸ examine integration of new AI tools into clinical workflows
▸ publish critical, student-driven analysis
                            

Six signals we monitor

Each research domain carries its own live signature. Select a channel to read it.

CHANNEL / CDS — LIVE

Clinical AI & Decision-Making

Analyzing how AI models — including emerging open-source LLMs and proprietary systems — are being integrated into clinical decision support. We review diagnostic reasoning engines, treatment recommendation algorithms, and triage tools, evaluating the evidence for accuracy, safety, real-world utility, and the innovation of new AI tools reshaping clinical workflows.

                

The team

Seven medical students at the University of Toronto — one year further into training, working alongside doctors and researchers across the field.

YS
M2
RESEARCHER / 01 — FOUNDER & RESEARCH DIRECTOR

Yazan Saleh

2nd Year Medical Student · University of Toronto

Yazan founded Inquiro to create a rigorous, student-driven platform for analyzing the role of artificial intelligence in medicine. His research interests span AI-augmented clinical decision-making, the evaluation of AI processing models for diagnostic and prognostic applications, the innovation and integration of new AI tools, and the translational potential of both proprietary and open-source machine learning models across radiology, surgery, medical education, and risk stratification in clinical practice. He leads Inquiro with a commitment to intellectual depth, collaborative inquiry, and advancing a critical understanding of how AI is reshaping the future of healthcare.

RESEARCH LEADSN = 06
SA
M2
RESEARCHER / 02

Shayan Ahmed

Research Lead
2nd Year Medical Student · University of Toronto
TK
M3
RESEARCHER / 03

Talal Khalid

Research Lead
3rd Year Medical Student · University of Toronto
MS
M2
RESEARCHER / 04

Ma’az Syed

Research Lead
2nd Year Medical Student · University of Toronto
SK
M2
RESEARCHER / 05

Samy Kannout

Research Lead
2nd Year Medical Student · University of Toronto
AS
M2
RESEARCHER / 06

Abdullah Al Sheikh Zein

Research Lead
2nd Year Medical Student · University of Toronto
IA
M2
RESEARCHER / 07

Ibrahim Ahmed Cherif

Research Lead
2nd Year Medical Student · University of Toronto
                   

Research console

An interactive index of what we study. Type a query, or start from a preset.

INQUIRO CONSOLE — v2.0
INQUIRO ▸
Inquiro console online. Query our research in clinical AI, surgery, radiology, medical education, risk stratification, AI ethics, or open-source models.
                

Join Inquiro

We're looking for medical students and collaborators interested in analyzing AI's role in healthcare through literature review, evidence synthesis, and critical research.