Bertrand Lebouché

Background: AI research increasingly depends on energy-intensive computation, yet energy use, greenhouse gas emissions, hardware life cycle burdens, and water consumption are rarely reported in a standardized way. This lack of reproducible environmental accounting limits comparisons across studies and obscures the trade-offs among model performance, infrastructure choices, carbon intensity, and cooling water demand. Objective: This study aimed to develop and describe an open-access, life cycle–informed AI Environmental Footprint Calculator and propose a minimum reporting dataset for transparent environmental disclosure in AI research. Methods: We developed a browser-based calculator that combines operational energy, regional grid carbon intensity, power usage effectiveness (PUE), water usage effectiveness, hardware embodied emissions, and workload-specific metrics for training and inference. The framework was evaluated in 3 representative scenarios: a single–graphics processing unit laboratory fine-tuning task, a midsized academic cluster workload, and a large-scale industrial training cycle. Outputs were compared with those of established tools to identify how boundary choices and parameter assumptions affect emission estimates. Results: Across the scenarios, the inclusion of PUE and hardware life cycle allocation increased reported emissions compared with operational-only estimates. In the small laboratory scenario, optimization reduced total emissions from approximately 0.07 kg carbon dioxide equivalents (CO ₂ e) to 0.05 kg CO ₂ e and improved the proposed label from C to B. In the midsized cluster scenario, carbon-aware scheduling reduced emissions from approximately 665 kg CO ₂ e/month to 450 kg CO ₂ e/month—a 32% reduction. In the large-scale scenario, shifting to renewable-backed, lower-PUE infrastructure reduced operational emissions by approximately 79% while increasing the importance of water-carbon trade-off reporting. Conclusions: The calculator provides a practical and transparent method for reporting AI environmental footprints using auditable parameters and publication-ready outputs. Routine disclosure of energy use, emissions, water use, hardware assumptions, and regional context can improve reproducibility and support more equitable and sustainable AI research evaluation.

Objectives: Material deprivation (MD), the inability to afford essentials, is linked with poorer HIV clinical outcomes. In Canada, migrants living with HIV are a growing group, yet little is known about their experience of MD, especially early in treatment. This study examined the prevalence, nature, sociodemographic and clinical correlates and longitudinal changes in MD among migrants living with HIV initiating antiretroviral therapy (ART). Methods: Analyses used data from 56 ART‐naïve migrants enrolled in the Antiretroviral Speed Access Program (ASAP), a cohort at the McGill University Health Centre, Montreal, Canada receiving access to rapid, no‐cost and onsite bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF). MD was assessed at Weeks 4 and 48 using the 17‐item Canadian Material Deprivation Index (CMDI), with deprivation defined as checking ≥2 items. Statistical tests included Fisher's exact, Wilcoxon signed‐rank, McNemar's exact and correlation coefficients. Results: At Week 4, 48 (85.7%) participants were materially deprived, most commonly reporting inability to cover an unexpected expense (87.5%), afford dental care (73.2%) or buy small gifts (73.2%). Week 4 MD was significantly associated with occupation ( p = 0.020) and migration status ( p = 0.016). No statistically significant associations were observed between MD and HIV clinical outcomes. Between Weeks 4 and 48, MD scores declined significantly (median: 7.5 to 4.0; p < 0.001), although 67.9% of participants remained deprived. Conclusions: Associated with occupation and migrant status, MD was highly prevalent and persistent among migrants living with HIV, despite partial improvement over time, underscoring the need for structural supports. Short‐term HIV clinical outcomes appeared unaffected, which may reflect access to rapid, no‐cost and onsite ART delivery.

Objectives: Migrants are disproportionately affected by HIV and face inequities in accessing care. This study examined the HIV care cascade among newly registered adults at the Chronic Viral Illness Service (CVIS). Methods: We conducted a retrospective cohort study of adults (≥18 years) living with HIV and newly registered at the CVIS, in Montréal, Canada, between January 1st, 2022, and December 31st, 2024. Sociodemographic, clinical and care cascade data were extracted from electronic records. Multivariable logistic regression models, adjusted for age, gender, region of birth and diagnosis type, examined associations between seven migration status categories and cascade outcomes. Results: Among 695 patients, 664 (95%) were born outside Canada and 528 (76%) were refugees/asylum seekers. Of these, 281/695 (40%) had a new HIV diagnosis. Among newly diagnosed refugees/asylum seekers, median time from diagnosis to linkage to care was 28 days (interquartile range [IQR]: 18–42). Median time from linkage to antiretroviral therapy (ART) prescription ranged from 0 (IQR: 0–0) to 18 (IQR: 9–27) days across groups. At 6 months, 618/695 (89%) were retained in care and 603/618 (98%) were on ART. At 12 months, 530/603 (88%) were in care and 511/530 (96%) were virally suppressed. In exploratory adjusted analyses, Canadian citizens had lower odds of retention (aOR 0.32, 95% CI 0.14–0.75) and viral suppression (aOR 0.33, 95% CI 0.15–0.75) at 12 months. Conclusions: Among newly diagnosed refugees/asylum seekers, linkage to care delays appear to have increased since pre‐pandemic estimates. Meaningful heterogeneity across migration groups highlights the limitations of treating migrants as a homogeneous population in HIV care.

Background: Acute infectious symptoms are a leading cause of pediatric emergency department visits in Canada, many of which are low acuity and 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.

BACKGROUND Artificial intelligence (AI) systems increasingly contribute to global energy consumption, greenhouse gas emissions, and water use, yet these impacts remain inconsistently measured and poorly reported in the research literature. Existing tools primarily focus on operational energy during model training and often omit critical contributors, including hardware lifecycle emissions, cooling overhead, and inference-level impacts. The absence of standardized, accessible, and reproducible environmental accounting frameworks limits comparability across studies and impedes environmental transparency in AI research. OBJECTIVE This study aims to develop and validate a transparent, lifecycle-informed framework for quantifying and reporting the environmental footprint of AI workloads and to propose a standardized environmental reporting and labeling approach suitable for routine inclusion in AI research publications. METHODS We developed the AI Environmental Footprint Calculator, a browser-based, open-access tool that integrates four core components of environmental accounting: (1) operational energy use derived from hardware power and runtime; (2) embodied emissions from hardware manufacturing and end-of-life amortized over device lifetime; (3) cooling and infrastructure overhead using configurable Power Usage Effectiveness (PUE) and optional Water Usage Effectiveness (WUE); and (4) inference-level granularity expressed as carbon dioxide equivalent (CO₂e) per 1,000 tokens or per request. The calculator incorporates regional and time-resolved grid carbon-intensity data and provides both minimal-input and expert modes. Validation was performed using three representative scenarios: a small laboratory fine-tuning task, a mid-sized academic research cluster, and a large-scale industrial training workload. Sensitivity analyses assessed the influence of key parameters, including grid emission factor, PUE, and hardware utilization. RESULTS Across validation scenarios, the calculator produced consistent and interpretable estimates of energy use, CO₂e emissions, and water consumption, while revealing systematic underestimation by operational-only tools. Inclusion of hardware lifecycle and cooling effects increased total emissions estimates by approximately 20%–40%, depending on workload scale. Scenario analyses demonstrated that algorithmic optimization, carbon-aware scheduling, and infrastructure choices can substantially reduce environmental impact. Based on these outputs, we introduce an AI Environmental Label (A–E grade) that benchmarks workloads using normalized carbon-intensity metrics for training and inference, enabling intuitive comparison across studies. CONCLUSIONS This work presents a comprehensive, reproducible framework for environmental accounting in AI research that bridges operational measurement with lifecycle assessment and inference-level reporting. By combining methodological rigor with accessibility, the proposed tool and labeling system support standardized environmental disclosure and informed decision-making. Adoption of such frameworks can facilitate transparency, comparability, and responsible scaling of AI systems across research environments.

BACKGROUND Acute infectious symptoms are leading causes of pediatric emergency department (ED) visits in Canada, many of which are low-acuity and 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 To adapt and co-construct 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 multi-phase, mixed-methods participatory study will be conducted at the Montreal Children’s Hospital in Montreal, Canada. A Co-Construction Committee comprised of youth, parents, caregivers, and partners will be engaged as co-researchers. Eligible participants will include: (1) youth aged 14-17 years and (2) parents and caregivers of children aged 0-17 years. The study comprises five phases. Phase 1 involves a qualitative needs assessment using focus groups with 20 participants to explore informational needs, preferences, and concerns about pediatric infections and AI chatbot use. Phase 2 focuses on co-constructing and validating CHAMP’s knowledge database through 3-5 workshops. Co-researchers 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. Co-researchers 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 employs collaborative ethnography to explore the process of participatory co-construction and its impact on CHAMP’s design. RESULTS Funding was secured in 2024 and REB approval was obtained in December 2024. As of December 2025, the Co-Construction 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 co-constructed, 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 ED visits, ensure at-risk children are directed to appropriate care, and improve patient and family healthcare experiences. CLINICALTRIAL NCT05789901

Uses of large language models (LLMs) in health chatbots are expanding into high-stakes clinical contexts, heightening the need for tools that 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.

Objectives: Sexual dysfunction (SD) is common among gay, bisexual and other men who have sex with men (GBM) with HIV, yet little is known about their views on SD care. We explored these views to inform patient‐centred SD care to improve care delivery and sexual health outcomes. Methods: Semi‐structured interviews were conducted in 2024 with 31 Montreal‐based GBM with HIV who experienced SD in the last 5 years (reduced libido, erectile dysfunction, premature/delayed ejaculation and/or pain during sex), covering SD care experiences and preferences. Thematic analysis was applied to interview transcripts. Results: Participants mostly reported reduced libido (83.9%) and erectile dysfunction (ED(80.6%), with over half (58.0%) experiencing multiple SDs concurrently. Themes regarding SD care experiences were (1) costs and benefits of ED medication, (2) limited benefits of testosterone replacement therapy, (3) mixed views on talk therapy (and a preference for group therapy), and (4) not seeking care due to questions of SD definition and normalcy. SD care preferences concerned both provider characteristics (identity, approach to patients and expertise) and care delivery (information provision, involvement and respect and access to diverse resources). Conclusions: Irrespective of the approach to SD care sought (medication or talk therapy), participants experienced limited success. For some, doubts about the severity of their SD impeded help‐seeking. SD care preferences for the provider (e.g., expertise) and care provided (e.g., information, patient involvement) offer paths to more patient‐centred care.