Dan Poenaru

sean.jeffries@mail.mcgill.ca

Masa Fukumoto

Maîtrise recherche - McGill

fu.masayuki@gmail.com

Sean Jeffries

Doctorat - McGill

shreenik.kundu@mila.quebec

Google Scholar

Shreenik Kundu

Doctorat - McGill

Hakima Laribi

Doctorat - Université de Sherbrooke

Co-superviseur⋅e :

Martin Vallières

hakima.laribi@mila.quebec

simon.g.provost@mcgill.ca

Github

Simon Provost

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Martin Vallières

Github

Google Scholar

Publications

Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection

Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shift… (voir plus)s between training and deployment or from heterogeneous performance where the model systematically underperforms for certain patient subgroups. Yet most post-hoc diagnostic methods evaluate reliability only at the population level, checking whether the deployment data resemble the training data and whether the model performs well on average, without accounting for heterogeneity that may cause some patients to be disproportionately affected. We propose a unified diagnostic pipeline that combines both perspectives. MED3pa first discovers interpretable low-confidence patient profiles, and Detectron is then applied within each profile to test whether its degradation is associated with a detectable harmful covariate shift. Across three clinical prediction tasks and three injected subgroup shifts in MIMIC-IV, we find that shift detectability itself is informative. An injected age shift, undetectable at the population level, was nonetheless localized to specific degraded profiles, whereas sex- and race-related shifts were detected broadly and therefore offered little guidance for identifying the most affected patients.

2026-10-12

arXiv (prépublication)

arxiv.org

Need for ethical use of artificial intelligence in humanitarian data collection to address aid shortfalls

Shreenik Kundu

Ayla Gerk

Duha Shellah

Dunya Moghul

Jean Marie Tshimula

Justina Seyi-Olajide

The global humanitarian crisis is escalating due to conflicts, climate-related disasters, and infection outbreaks.1 Recent cuts by the USA a… (voir plus)nd other high-income donors to international aid, including considerable reductions in foreign assistance, have abruptly diminished crucial support for global health programmes, food security, and disaster relief.2 With overburdened humanitarian systems facing worsening resource constraints, data-driven aid allocation has become increasingly important for survival worldwide.

2026-08-19

The Lancet Digital Health (publié)

Evaluative Stance Toward Artificial Intelligence in High-Quartile Medical Journals (2021– 2026): Large-Scale LLM-Assisted Computational Content Analysis

Lixing Wang

Abstract Background Medical-AI publications do more than report technical performance; they also frame AI as beneficial, uncertain, or risky… (voir plus). How this evaluative stance has changed across the medical literature is not well characterized. Objective To characterize evaluative stance in published medical-AI discourse abstracts from January 2021 through April 2026 and examine variation over time, concern themes, failure mechanisms, specialties, first-author geography, and publication format. Methods We conducted an LLM-assisted computational content analysis of medical-AI abstracts from first- and second-quartile medical journals. Of 97,492 post-cutoff Q1/Q2 records entering the prefilter, 16,759 were retained as discourse or evaluative. Claude Sonnet 4.6 assigned 16,749 valid stance classifications using Alarm, Caution, Neutral, Cautious Optimism, and Advocacy. Annual analyses used 16,747 records dated 2021–2026. Critical stance was Alarm plus Caution and indexed evaluative scrutiny, rather than opposition or author psychology. Each LLM step was validated against blinded human coding by one author: prefilter Cohen κ=0.51, stance quadratic-weighted κ=0.79 (95% CI 0.72–0.84) for codable, in-scope records, specialty κ=0.75, and mechanism axes κ=0.84 (model type) and κ=0.57 (failure mode). Results Advocacy declined from 2.9% in 2021 to 0.6% in partial 2026, while Cautious Optimism remained the majority stance. Among 16,749 valid classifications, 30.8% were critical. Critical share increased from 25.4% to 32.6%, a 7.25-percentage-point increase based on unrounded estimates. Among critical records, patient safety remained the most prevalent concern. Hallucination/errors increased by 30.9 percentage points. Regulation declined by 22.0 percentage points and ethics/bias by 8.1 percentage points in prevalence share; these declines do not necessarily indicate lower publication counts. Within the hallucination/error theme, factual error was more common than fabrication. Fabrication estimates should be treated as an upper bound, because failure-mode agreement was moderate. Specialty patterns were heterogeneous. Critical rate was inversely associated with FDA cleared-device availability (Spearman ρ=−0.65, two-sided p=0.004), which does not measure adoption, deployment, maturity, or clinical use. First-author geography described publication metadata and discourse, not national attitudes or research quality. Reviews were the least critical and most favourable format. In exploratory forward-validation, 2 of 78 early Advocacy predictions were fully borne out, although the analysis was single-rater and retrieval-dependent. Conclusions Published medical-AI abstracts became modestly less promotional and more focused on specific errors and safety concerns. Unqualified promotion declined, but qualified favourable framing remained dominant, and the rise in critical stance was modest. Concern moved toward errors and patient safety, with factual error discussed more often than fabrication. These findings describe published discourse, not AI capability or whether the evaluations were correct.

2026-07-26

medRxiv (prépublication)

Artificial intelligence-assisted ganglion cell detection in Hirschsprung's disease: A comparative evaluation of two deep learning approaches

E Wang

Karl Grenier

Peter Savadjiev

Background. Definitive diagnosis of Hirschsprung's disease (HD) requires pathological identification of enteric ganglion cells. This process… (voir plus) is time-consuming and subject to inter-observer variability. Artificial intelligence (AI) tools have the potential to standardize and accelerate this workflow, but no study has determined which AI approach best serves intraoperative HD pathology diagnostics. Method. This study compared the U-Net and You Only Look Once version 26 (YOLO26) frameworks for ganglion cell detection using a single-centre retrospective dataset of 54 whole-slide images (WSIs) from rectal biopsies. WSIs were tiled into 397,731 image patches (128x128 pixels), further partitioned into training (70%), validation (15%), and testing (15%) sets. Models were evaluated on tile- and patient-level diagnostic metrics and processing latency. Results. The U-Net achieved a tile-level sensitivity of 82.9%, showing no statistically significant difference compared to YOLO26 (79.1%; p = 0.097). However, YOLO26 demonstrated a statistically significant advantage in tile-level specificity (96.1% vs. 93.9%; p < 0.001) and reduced mean inference latency (7.64 ms vs. 11.57 ms/tile). At the patient level, both models achieved 100% diagnostic sensitivity. Despite low patient-level specificity (0.0% U-Net; 11.8% YOLO26), the tissue-level diagnostic burden of false positives was 6.00% for U-Net and 3.50% for YOLO26. Conclusion. The U-Net is preferred when nominal gains in sensitivity are prioritized, while the YOLO26 is an alternative that optimizes efficiency and false positive suppression. Both models serve as robust screening filters to augment the pathologist's workflow and should be selected based on workflow requirements. Prospective validation on larger, multi-centre datasets is required before clinical implementation.

2026-06-11

medRxiv (accepté)

Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study

Jia Lin

Nikhil Jaiswal

Yuanchao Ma

Bertrand Lebouché

Sebastian Villanueva

Sofiane Achiche

David Lessard

Kim Engler

Simon Berthelot

David Llewellyn Buckeridge

Leo Anthony Celi

Isabelle Gagnon

Jocelyn Gravel

Laurie Hayes Plotnick

Marie‐Pascale Pomey

Zoua M. Vang

Esli Osmanlliu

Aida Fouad Margaux Elisabete JoAnne Annie-Danielle Maria Brenna Lisa Paul Sonia Amy Kofi Arleth Marie-José Berberovic

Background: Acute infectious symptoms are a leading cause of pediatric emergency department visits in Canada, many of which are low acuity a… (voir plus)nd could be safely managed at home. Artificial intelligence (AI) chatbots offer a promising avenue for delivering accessible, evidence-based guidance to support families in managing these symptoms. Objective: This study aims to adapt and coconstruct CHAMP (CHatbot to Assist the Management of Pediatric patients), an AI chatbot to support patients and families with acute pediatric infectious symptoms. CHAMP aims to deliver timely, tailored, and validated health information to support safe at-home self-management and informed care-seeking. Methods: This multiphase, mixed methods participatory study will be conducted at the Montreal Children's Hospital in Montreal, Quebec, Canada. A coconstruction committee comprised of youth, parents, caregivers, and partners will be engaged as coresearchers. Eligible participants will include (1) youth aged 14-17 years and (2) parents and caregivers of children aged 0-17 years. The study comprises 5 phases. Phase 1 involves a qualitative needs assessment using focus groups with 20 participants to explore their informational needs, preferences, and concerns regarding pediatric infections and the use of AI chatbots. Phase 2 focuses on coconstructing and validating CHAMP's knowledge database through 3-5 workshops. Coresearchers will review pediatric clinical guidelines, map care questions and decision-making processes, and shape CHAMP's conversational framework. Phase 3 consists of iterative prototyping and testing through 3-5 workshops. Coresearchers will engage in prototyping and scenario testing, alongside preliminary usability and acceptability assessments. Phase 4 examines equity and accessibility through focus groups with 20 participants at risk of digital exclusion, as well as multilingual evaluation of an automated large language model-based translation layer. Phase 5 uses collaborative ethnography to explore the process of participatory coconstruction and its impact on CHAMP's design. Results: Funding was secured in 2024, and Research Ethics Board approval was obtained in December 2024. As of December 2025, the coconstruction committee is being assembled, and Phase 1 recruitment is underway. Conclusions: This study will produce a functioning CHAMP prototype grounded in participatory, equitable, and responsible pediatric AI development. Findings will inform usability testing and an implementation-effectiveness evaluation, contributing to best practices for pediatric-centered AI health tools. By providing timely, tailored, and validated health information on acute infections, CHAMP may support safe at-home self-management, reduce preventable emergency department visits, ensure at-risk children are directed to appropriate care, and improve patient and family health care experiences.

2026-05-25

JMIR Research Protocols (publié)

Assessing Language Bias in Pediatric Surgical Systematic Reviews: A Meta-epidemiological Study.

Dunya Moghul

Elena Guadagno

Shreenik Kundu

Robert Baird

2026-01-31

Journal of Pediatric Surgery (publié)

Patient safety culture in the operating room of African hospitals: a systematic review

Jacques Fadhili Bake

Naïcen Ghanmi

Elena Guadagno

K. M. Claude

Tsongo Kibendelwa Zacharie

Patient safety in operating rooms has globally improved through interventions such as the World Health Organization (WHO) Surgical Safety Ch… (voir plus)ecklist and multidisciplinary team training. However, while evidence from high-income countries is well documented, there remains limited consolidated knowledge on the understanding, application, and effectiveness of safety culture interventions in African surgical settings, which this review seeks to address. This systematic review examined factors and protocols affecting surgical safety in African operating rooms. We hypothesized that persistent systemic barriers undermine safety culture despite adoption of global measures. Following PRISMA 2020, we searched eight databases (Medline, Embase, Cochrane, Africa-Wide, CINAHL, Global Health, Global Index Medicus, Web of Science) from inception to 5 December 2024, using variations of text words present in the title, abstract, or keyword fields, alongside relevant subject headings, to identify articles addressing surgical safety and culture throughout Africa. Included studies involved operating room professionals in African countries and used quantitative, qualitative, or mixed-methods designs. We excluded non-operating room settings, patient-only studies, inaccessible full texts, reviews, editorials, letters, conference abstracts, and duplicates. Two reviewers independently screened and appraised studies using the Mixed Methods Appraisal Tool. Findings were synthesized narratively with subgroup analysis by study type and theme. Out of 9,875 identified records, 22 studies from 12 African countries (2014–2024) met inclusion criteria, with Ethiopia contributing the highest number (n = 4). Various assessment tools, including the Hospital Survey on Patient Safety Culture, the Safety Attitudes Questionnaire, and the National Surgical, Obstetric, and Anaesthesia Plans interview manual, revealed recurring challenges: inadequate non-punitive responses to errors, communication barriers, hierarchical structures, and resource constraints. Four interventions showed promise: implementation and training on the WHO Surgical Safety Checklist, Safe Surgery 2020 initiatives, Non-Technical Skills for Surgeons training, and multidisciplinary training. The heterogeneity of study designs, sample sizes, and outcome measures limited direct comparisons and precluded meta-analysis. Nonetheless, the review highlights persistent barriers and emerging opportunities to strengthen patient safety culture in African operating rooms. While the WHO Surgical Safety Checklist remains valuable, sustainable progress requires multi-level strategies that address systemic constraints and incorporate context-sensitive adaptations. PROSPERO, CRD42024627076.

2026-01-28

Patient Safety in Surgery (publié)

Large language models for electronic health records in pediatric and surgical care: a systematic review.

Waseem Abu-Ashour

Elena Guadagno

2025-12-31

Journal of Pediatric Surgery (publié)

Suspected Biliary Atresia in Brazil: Impact of Regional Healthcare Variations on Diagnostic Timeliness

Luiza Telles

Paulo Henrique Moreira Melo

Ana Maria Bicudo Diniz

Gabriele Lech

Ayla Gerk

Lauren Kratky

David P. Mooney

Joaquim Bustorff-Silva

2025-12-31

Journal of Pediatric Surgery Open (publié)

The Impact of Pediatric Surgery Global Travel Fellowships: A Study by the Canadian Association of Paediatric Surgeons Global Partnership Committee.

Sacha Williams

Natasha Bejjani

Elena Guadagno

Robert Baird

Shahrzad Joharifard

Melanie Morris

Robin Petroze

Sherif Emil

2025-12-31

Journal of Pediatric Surgery (publié)

Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots

Nikhil Jaiswal

Yuanchao Ma

Bertrand Lebouché

Marie-Pascale Pomey

Sofiane Achiche

David Lessard

Kim Engler

Zully Montiel

Hector Acevedo

Rodrigo Rosa Gameiro

Leo Anthony Celi

Esli Osmanlliu

Uses of large language models (LLMs) in health chatbots are expanding into high-stakes clinical contexts, heightening the need for tools tha… (voir plus)t are evidence-based, accountable, accurate, and patient-centred. This conceptual, practice-informed Perspective reflects on engaging patients and non-academic partners for the responsible integration of LLMs, grounded in the co-construction of MARVIN (for people living with HIV) and in an emerging collaboration with MIT Critical Data. Organised by the Software Development Life Cycle, we describe: conception/needs assessment with patient partners to identify use cases, acceptable trade-offs, and privacy expectations; development that prioritises grounding via vetted sources, structured human feedback, and data-validation committees including patient partners; testing and evaluation using patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) chosen in collaboration with patients to capture usability, acceptability, trust, and perceived safety, alongside task performance and harmful-output monitoring; and implementation via diverse governance boards, knowledge-mobilisation materials to set expectations, and risk-management pathways for potentially unsafe outputs. Based on our experience with MARVIN, we recommend early and continuous engagement of patients and non-academic partners, fair compensation, shared decision-making power, transparent decision logging, and inclusive, adaptable governance that can evolve with changing models and standards. These lessons highlight how patient partnership can directly shape chatbot design and oversight, helping teams align LLM-enabled tools with patient-centred goals while building accountable, safe, and equitable systems. Health chatbots powered by large language models (LLMs) can make medical information more accessible, but most are developed without meaningful input from the people who will use them. This risks unsafe answers, hidden bias, and tools that mainly work for privileged groups. Our team built a chatbot called MARVIN to support people living with HIV, and we are now adapting it for cancer care and children’s health. Patients, caregivers, and community partners shaped what MARVIN should do, chose which sources it should trust, and tested early versions. Their feedback led to concrete improvements including clearer language, more relevant features, and safeguards against misinformation. We are also partnering with MIT Critical Data, which brings patients, members of the public, clinicians, engineers, and policymakers together at events to find and fix bias in medical AI. We have learned that technical fixes alone are not enough: trust, fairness, and accountability require active involvement of diverse users at every stage. Based on these lessons, we recommend: (1) including patients and non-academic partners from the start so their insights can shape core design decisions; (2) compensating them fairly so participation is sustainable; (3) giving them real decision-making power so their input is not tokenistic; and (4) being transparent about the limits of AI so expectations are realistic. In our experience, responsible health AI depends on the lived expertise of the people it serves.

2025-12-21

Research Involvement and Engagement (publié)

Synthetic Validation of Pediatric Trust Instruments using Persona-Driven Large Language Models

Ella Boone

Katya Loban

Elena Guadagno

Trust is foundational to patient-physician relationships and is associated with improved care-seeking and adherence in primary care. However… (voir plus), validated trust instruments for pediatric emergency and surgical contexts are lacking, and traditional instrument development is slow and resource-intensive. Large language models (LLMs) could streamline the validation process by serving as scalable, systematic expert panel surrogates. We developed four new trust assessment instruments: two for patient-families and two for physicians. Two-phase content validation was conducted using two parallel synthetic and human expert panels. Synthetic panels consisted of three persona-prompted LLMs (Claude Sonnet 4, GPT-5, Grok4). Human panels served as traditional comparators. Scale-Content Validity Index (S-CVI) and Fleiss’ kappa (k) acceptance thresholds were set at ≥0.80. Combined human–synthetic expert panels revealed substantial inter-rater reliability across all instruments. Fleiss’ kvalues for dimensional validation were: patient-family = 0.84 (95% CI [0.72, 0.96]), physician = 0.87 (95% CI [0.72, 1.00]);contextual validation: patient-family = 0.83 (95% CI [0.73, 0.93]), physician = 0.88 (95% CI [0.80, 0.96]). All instruments exceeded S-CVI ≥0.80 thresholds across both validation phases. Persona-prompted LLMs demonstrated comparable validity outcomes to human experts while accelerating validation timelines from months to weeks. Future research needs to evaluate this approach across psychometric testing phases. This synthetic instrument validation methodology offers a scalable blueprint for healthcare measurement development, enabling faster creation of validated tools to support evidence-based patient care.

2025-11-26

MedRxiv (prépublication)