Publications

Use of Artificial Intelligence in Adolescents’ Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions
Gauri Sharma
Mark J Yaffe
Pooria Ghadiri
Rushali Gandhi
Laura Pinkham
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Abstract Background Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate manageme… (see more)nt are needed to decrease mortality and morbidity. Artificial intelligence’s (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents’ mental health. Objective This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents’ mental health care. Methods We used the Arksey and O’Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Results Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. Conclusions In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.
2025-06-05
JMIR Mental Health (published)
doi.org
Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions
Gauri Sharma
Mark J Yaffe
Pooria Ghadiri
Rushali Gandhi
Laura Pinkham
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to dec… (see more)rease mortality and morbidity. Artificial intelligence’s (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents’ mental health. This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents’ mental health care. We used the Arksey and O’Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.
2025-06-05
JMIR Mental Health (published)
doi.org
Clarifying a working definition for ‘precision communication’: a scoping review of medical literature on communication
Bao-Lam Pham
Brigitte N. Durieux
Amanda Bianco
Corinne Cécyre-Chartrand
Elena Guadagno
Amalia M. Issa
Dan Poenaru
2025-06-04
Personalized Medicine (published)
doi.org
Policy context and digital development: a comparative study of trajectories in 4 Canadian academic health centers over 30 years
Aude Motulsky
Susan Usher
Pascale Lehoux
Catherine Régis
Trish Reay
Paul Hebert
Lise Gauvin
Alain Biron
G. Ross Baker
Marie-Pierre Moreault
Johanne Préval
Jean-Louis Denis
The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various… (see more) paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development. AHCs play a leadership role within health systems in data-driven improvement. This retrospective case study draws on documentary, observational, and interview data to compare digitalization efforts over 3 decades in 4 AHCs in the province of Quebec (Canada). At system level, strategy shifted from supporting multilayered development that encouraged bottom-up initiatives in the first decade of the 2000s, to harmonizing clinical information systems in a highly prescriptive manner after 2010. AHCs experienced the shift differently according to concurrent impacts of health system restructuring, and internal choices around electronic health record (EHR) systems and implementation priorities. Digital maturity remained low in all 4 AHCs. Coordination between system strategies and organizational strategies in AHCs was neglected in early digital development in Québec and improved only after an intense period of prescription and resistance. Confrontation highlighted tensions around different objectives at AHC and system level, competing missions within AHCs, and trade-offs between relying on commercial EHRs and developing publicly owned systems, all of which ultimately influence EHR implementation. The different experiences of focal organizations with digitalization underline the importance of adapting national strategies and providing support to implementers, building on acquired strengths, and arriving at the right balance of guidance from the top and autonomy to develop innovative capacities.
2025-06-04
Journal of the American Medical Informatics Association : JAMIA (published)
doi.org
Multi-timescale reinforcement learning in the brain
Paul Masset
Pablo Tano
HyungGoo R. Kim
Athar N. Malik
Alexandre Pouget
Naoshige Uchida
To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive… (see more) behavior can be learned through reinforcement learning1, a class of algorithms that has been successful at training artificial agents2–6 and at characterizing the firing of dopamine neurons in the midbrain7–9. In classical reinforcement learning, agents discount future rewards exponentially according to a single time scale, controlled by the discount factor. Here, we explore the presence of multiple timescales in biological reinforcement learning. We first show that reinforcement agents learning at a multitude of timescales possess distinct computational benefits. Next, we report that dopamine neurons in mice performing two behavioral tasks encode reward prediction error with a diversity of discount time constants. Our model explains the heterogeneity of temporal discounting in both cue-evoked transient responses and slower timescale fluctuations known as dopamine ramps. Crucially, the measured discount factor of individual neurons is correlated across the two tasks suggesting that it is a cell-specific property. Together, our results provide a new paradigm to understand functional heterogeneity in dopamine neurons, a mechanistic basis for the empirical observation that humans and animals use non-exponential discounts in many situations 10–14, and open new avenues for the design of more efficient reinforcement learning algorithms.
2025-06-03
Nature (published)
doi.org
Trophic Interactions Are Key to Understanding the Effects of Global Change on the Distribution and Functional Role of the Brown Bear
Pablo M. Lucas
Wilfried Thuiller
Lauren Talluto
Ester Polaina
Jörg Albrecht
Nuria Selva
Marta De Barba
Vincenzo Penteriani
Maya Guéguen
Niko Balkenhol
Trishna Dutta
Ancuta Fedorca
Shane C. Frank
Andreas Zedrosser
Ivan Afonso‐Jordana
Hüseyin Ambarlı
Fernando Ballesteros
Andriy‐Taras Bashta
Cemal Can Bilgin
Neda Bogdanović … (see 67 more)
Edgars Bojārs
Katarzyna Bojarska
Natalia Bragalanti
Henrik Brøseth
Mark W. Chynoweth
Duško Ćirović
Paolo Ciucci
Andrea Corradini
Daniele De Angelis
Miguel de Gabriel Hernando
Csaba Domokos
Aleksander Dutsov
Alper Ertürk
Stefano Filacorda
Lorenzo Frangini
Claudio Groff
Samuli Heikkinen
Bledi Hoxha
Djuro Huber
Otso Huitu
Georgeta Ionescu
Ovidiu Ionescu
Klemen Jerina
Ramon Jurj
Alexandros A. Karamanlidis
Jonas Kindberg
Ilpo Kojola
José Vicente López‐Bao
Peep Männil
Dime Melovski
Yorgos Mertzanis
Paolo Molinari
Anja Molinari‐Jobin
Andrea Mustoni
Javier Naves
Sergey Ogurtsov
Deniz Özüt
Santiago Palazón
Luca Pedrotti
Aleksandar Perović
Vladimir N. Piminov
Ioan‐Mihai Pop
Marius Popa
Maria Psaralexi
Pierre‐Yves Quenette
Georg Rauer
Slaven Reljic
Eloy Revilla
Urmas Saarma
Alexander P. Saveljev
Ali Onur Sayar
Çagan H. Şekercioğlu
Agnieszka Sergiel
George Sîrbu
Tomaž Skrbinšek
Michaela Skuban
Anil Soyumert
Aleksandar Stojanov
Egle Tammeleht
Konstantin Tirronen
Aleksandër Trajçe
Igor Trbojević
Tijana Trbojević
Filip Zięba
Diana Zlatanova
Tomasz Zwijacz‐Kozica
Laura J. Pollock
ABSTRACT Biotic interactions are expected to influence species' responses to global changes, but they are rarely considered across broad spa… (see more)tial extents. Abiotic factors are thought to operate at larger spatial scales, while biotic factors, such as species interactions, are considered more important at local scales within communities, in part because of the knowledge gap on species interactions at large spatial scales (i.e., the Eltonian shortfall). We assessed, at a continental scale, (i) the importance of biotic interactions, through food webs, on species distributions, and (ii) how biotic interactions under scenarios of climate and land‐use change may affect the distribution of the brown bear ( Ursus arctos ). We built a highly detailed, spatially dynamic, and empirically sampled food web based on the energy contribution of 276 brown bear food species from different taxa (plants, vertebrates, and invertebrates) and their ensemble habitat models at high resolution across Europe. Then, combining energy contribution and predicted habitat of food species, we modelled energy contribution across space and included these layers within Bayesian‐based models of the brown bear distribution in Europe. The inclusion of biotic interactions considerably improved our understanding of brown bear distribution at large (continental) scales compared with Bayesian models including only abiotic factors (climate and land use). Predicted future range shifts, which included changes in the distribution of food species, varied greatly when considering various scenarios of change in biotic factors, providing a warning that future indirect climate and land‐use change are likely to have strong but highly uncertain impacts on species biogeography. Our study confirmed that advancing our understanding of ecological networks of species interactions will improve future projections of biodiversity change, especially for modelling species distributions and their functional role under climate and land‐use change scenarios, which is key for effective conservation of biodiversity and ecosystem services.
2025-06-03
Global Change Biology (published)
doi.org
Galaxy cluster characterization with machine learning techniques
M. Sadikov
J. Hlavacek-Larrondo
L. Perreault-Levasseur
C. L. Rhea
M. McDonald
M. Ntampaka
J. Zuhone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (see more)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-06-02
The Astrophysical Journal (published)
doi.org
arxiv.org
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
Xingfang Wu
Heng Li
Foutse Khomh
Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications … (see more)and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep learning models to capture the semantic or sequential information in the log data and detect anomalous runtime behaviors. However, the impacts of these different types of information are not clear. In addition, existing approaches have not captured the timestamps in the log data, which can potentially provide more fine-grained temporal information than sequential information. In this work, we propose a configurable transformer-based anomaly detection model that can capture the semantic, sequential, and temporal information in the log data and allows us to configure the different types of information as the model's features. Additionally, we train and evaluate the proposed model using log sequences of different lengths, thus overcoming the constraint of existing methods that rely on fixed-length or time-windowed log sequences as inputs. With the proposed model, we conduct a series of experiments with different combinations of input features to evaluate the roles of different types of information in anomaly detection. When presented with log sequences of varying lengths, the model can attain competitive and consistently stable performance compared to the baselines. The results indicate that the event occurrence information plays a key role in identifying anomalies, while the impact of the sequential and temporal information is not significant for anomaly detection in the studied public datasets. On the other hand, the findings also reveal the simplicity of the studied public datasets and highlight the importance of constructing new datasets that contain different types of anomalies to better evaluate the performance of anomaly detection models.
2025-06-02
Automated Software Engineering (published)
doi.org
arxiv.org
Advancing global antifungal development to combat invasive fungal infection
Xiu-Li Wang
Jun Ding
Koon Ho Wong
Chen Ding
Chang-Bin Chen
Wen-Juan Wu
Ningning Liu
2025-05-31
hLife (published)
doi.org
Continual Learning in Vision-Language Models via Aligned Model Merging
Ghada Sokar
Gintare Karolina Dziugaite
Anurag Arnab
Ahmet Iscen
Pablo Samuel Castro
Cordelia Schmid
Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors pl… (see more)asticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.
2025-05-31
arXiv (published)
doi.org
arxiv.org
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Ankur Garg
Xuemin Yu
Hassan Sajjad 0001
S Ebrahimi Kahou
Uncovering emergent concepts across transformer layers remains a significant challenge because the residual stream linearly mixes and duplic… (see more)ates information, obscuring how features evolve within large language models. Current research efforts primarily inspect neural representations at single layers, thereby overlooking this cross-layer superposition and the redundancy it introduces. These representations are typically either analyzed directly for activation patterns or passed to probing classifiers that map them to a limited set of predefined concepts. To address these limitations, we propose \gls{clvqvae}, a framework that uses vector quantization to map representations across layers and in the process collapse duplicated residual-stream features into compact, interpretable concept vectors. Our approach uniquely combines top-
2025-05-31
arXiv (published)
doi.org
arxiv.org
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Anthony Gosselin
Ge Ya Luo
Luis Lara
Florian Golemo
D. Nowrouzezahrai
Liam Paull
Alexia Jolicoeur-Martineau
Christopher Pal
Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes … (see more)due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash video generation model that conditions on signals such as bounding boxes, crash types, and an initial image frame. Our approach enables counterfactual scenario generation where minor variations in input can lead to dramatically different crash outcomes. To support fine-grained control at inference time, we leverage classifier-free guidance with independently tunable scales for each conditioning signal. Ctrl-Crash achieves state-of-the-art performance across quantitative video quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a human-evaluation of physical realism and video quality compared to prior diffusion-based methods.
2025-05-31
arXiv (published)
doi.org
arxiv.org