International Workshop on

Artificial Intelligence in Mental Health

29-30 December 2026 | NIT Goa, India

International Workshop · NIT Goa

Artificial Intelligence in Mental Health

Multimodal Biomarkers, Explainable Models, and Precision Psychiatry

Bringing together researchers, clinicians, engineers, and AI practitioners to explore how trustworthy computational methods can support a more objective, individualized, and clinically meaningful understanding of mental health.

Important Dates

Paper Submission Deadline: 30th Nov. 2026
Acceptance Notification: 10 days after submission
CRC Submission: 15 days after acceptance
Registration Deadline: 30 days after acceptance

About the workshop

Connecting artificial intelligence with clinically meaningful mental healthcare

Mental health conditions are complex and heterogeneous. Symptoms can overlap across disorders, while people with the same diagnosis may differ in their biology, behaviour, treatment response, and long-term outcomes. This workshop will explore how AI can help researchers study that complexity responsibly.

Contemporary mental-health research increasingly draws on electroencephalography (EEG), neuroimaging, speech and language, clinical assessments, wearable sensors, smartphone-derived information, and molecular measurements. Artificial intelligence offers methods for analysing these diverse data sources, discovering patterns, and investigating how biological, behavioural, environmental, and clinical factors interact.

The workshop will move beyond the narrow framing of “patient versus healthy control” classification. It will emphasize multimodal characterization, early identification, differential diagnosis, patient stratification, symptom trajectories, treatment-response prediction, relapse monitoring, and personalized intervention.

Particular attention will be given to overlapping presentations including psychosis, affective disorders, anxiety, trauma-related conditions, and psychiatric comorbidity and to the challenge of translating promising computational results into reliable, fair, and interpretable tools.

Workshop at a glance

Multimodal mental-health data and biomarkers
EEG, neuroimaging, speech, behaviour, and digital phenotyping
Machine learning, deep learning, transformers, and graph learning
Explainable AI and clinically interpretable discovery
Transdiagnostic analysis and differential diagnosis
Precision psychiatry and longitudinal prediction
Responsible, reproducible, and equitable AI

Workshop objectives

The workshop is intended to foster interdisciplinary discussion and equip participants with a clearer view of the opportunities, limitations, and responsible research practices associated with AI in mental health.

01 Introduce current AI approaches for mental-health assessment and computational psychiatry.

02 Explore neurophysiological, neuroimaging, speech, behavioural, digital, clinical, and molecular biomarkers.

03 Discuss multimodal fusion strategies and learning across complementary data types.

04 Examine explainability, uncertainty estimation, and the difference between predictive features and validated biomarkers.

05 Investigate AI for differential diagnosis, patient stratification, prognosis, treatment response, and relapse monitoring.

06 Highlight validation, reproducibility, fairness, privacy, ethics, and pathways to clinical translation.

Download Brochure

Major themes and technical areas

The workshop welcomes conceptual, methodological, and application-oriented contributions spanning computing, neuroscience, biomedical engineering, psychology, psychiatry, and digital health.

AI foundations for mental health

Computational psychiatry, clinical phenotyping, diagnostic frameworks, cohort design, data quality, confounding, and longitudinal assessment.

EEG and electrophysiological biomarkers

Spectral features, event-related potentials, entropy, complexity, connectivity, microstates, dynamic neural activity, and physiological signals.

Neuroimaging and brain networks

Structural and functional MRI, diffusion imaging, fNIRS, brain morphology, functional connectivity, connectomics, and radiomics.

Speech, language, and behaviour

Voice and prosody, speech rhythm, semantic coherence, natural-language processing, facial and motor behaviour, cognition, and social interaction.

Psychosis and cross-diagnostic AI

Schizophrenia-spectrum conditions, first-episode psychosis, high-risk states, bipolar disorder, psychotic depression, and differential diagnosis.

Multimodal data fusion

EEG–MRI, EEG–speech, clinical–wearable, imaging–genetics, joint embeddings, multimodal attention, missing-modality learning, and modality reliability.

Mood, anxiety, and trauma-related conditions

Depression, bipolar mood-state prediction, anxiety, PTSD, obsessive-compulsive conditions, neurodevelopmental conditions, and comorbidity.

Precision psychiatry and prediction

Patient stratification, individualized risk, prognosis, treatment response and resistance, symptom trajectories, relapse prediction, and personalized intervention.

Explainable AI and biomarker discovery

SHAP, LIME, saliency maps, feature attribution, attention visualization, Grad-CAM, graph explanations, counterfactuals, and uncertainty estimation.

Who Should Attend?

The workshop is designed for participants interested in developing, evaluating, or applying AI methods to mental-health research and care.

AI and machine-learning researchers

Biomedical engineers

EEG and signal-processing researchers

Digital-health professionals

Bioinformatics researchers

Neuroscientists

Neuroimaging researchers

EEG and signal-processing researchers

Healthcare technology teams

Faculty and research scientists

Paper Submission and Registration Information

The International Workshop on Artificial Intelligence in Mental Health (AIMH), organized under the CARES conference initiative, will be conducted in hybrid mode, enabling participation by both in-person and online attendees. Researchers, academicians, industry professionals, and practitioners are invited to submit original research contributions aligned with the workshop themes.

1. Paper Submission

All manuscripts must be submitted electronically through the Microsoft Conference Management Toolkit (CMT) portal using the designated AIMH workshop track. Authors are requested to follow the submission guidelines, formatting requirements, and instructions provided on the portal. The LaTeX and Word templates can be downloaded from the following links:

Word template LaTeX template

Paper Submission Link: https://cmt3.research.microsoft.com/CARES2027/Track/8/Submission/Create

Authors should ensure that their submissions:

Present original, unpublished research and clearly state the contribution and significance of the work.
Address topics relevant to artificial intelligence in mental health, computational psychiatry, digital mental healthcare, multimodal learning, biomedical signal processing, explainable AI, or related areas.
Follow the applicable manuscript formatting, authorship, research ethics, and plagiarism policies.
Include complete and accurate author information and contact details.

All submitted papers will undergo evaluation based on originality, technical quality, methodological rigor, relevance to the workshop, and potential scientific impact. Publication opportunities for selected papers will be governed by the applicable peer-review process and the requirements of the respective publication outlets.

2. Registration and Participation

Registration Fees

The registration fees, including all, currently indicated for authors and attendee are as follows:

Click on 'Pay Now' to pay Registration Fee

(Use UPI to avoid transaction Charges)

Authors whose papers are accepted must complete the applicable registration formalities within the prescribed deadline. At least one author should register and participate in the workshop in accordance with the organizers’ presentation and registration policies.

Both in-person and online participation will be supported in accordance with the hybrid workshop arrangements. Registered presenters should follow the instructions provided by the organizing committee regarding presentation schedules, online meeting access, presentation duration, and technical requirements.

3. Important Instructions

Submission: Submit manuscripts exclusively through the designated workshop track in the CMT portal.
Deadlines: Check the official workshop webpage for the current submission, notification, and registration deadlines.
Fees and payment: Confirm the applicable registration category and payment instructions before making a payment.
Acceptance and publication: Paper acceptance, workshop presentation, and publication are subject to their respective requirements.
Updates: Authors should regularly consult the official AIMH workshop webpage for announcements and changes to submission or registration procedures.

For any clarification regarding submission, registration, fees, or participation, authors should contact the workshop organizing committee through the official workshop communication channels.

Publication Information

The International Workshop on Artificial Intelligence in Mental Health (AIMH 2026), organized as part of the CARES 2027 initiative at the National Institute of Technology Goa, India, aims to provide publication opportunities for high-quality research contributions in artificial intelligence, computational psychiatry, and digital mental healthcare.

Subject to peer-review outcomes, editorial evaluation, and the respective publishers’ approval, selected papers will be considered for publication through the following two publication avenues:

1. Cambridge Scholars Publishing

Selected high-quality contributions may be considered for publication in an edited scholarly volume with Cambridge Scholars Publishing. The volume will focus on emerging research and interdisciplinary advances in AI-driven mental health assessment, multimodal biomarker analysis, explainable artificial intelligence, computational psychiatry, and precision mental healthcare. Final selection will depend on the quality, originality, relevance, and suitability of the manuscripts, as well as the publisher’s editorial approval.

2. Lecture Notes in Electrical Engineering (LNEE), Springer Nature

Selected research papers may also be considered for publication in Springer Nature’s Lecture Notes in Electrical Engineering (LNEE) series, subject to the required peer-review process, editorial evaluation, and formal approval by Springer Nature. Contributions should present original research, sound methodologies, rigorous experimental validation, and meaningful advances in AI-enabled mental healthcare, biomedical signal processing, neuroimaging, multimodal learning, and explainable or trustworthy AI.

Selection and Publication Policy

All submissions will be evaluated on their originality, technical quality, scientific significance, relevance to the workshop themes, methodological rigor, clarity of presentation, and potential impact. The final allocation of papers between the publication avenues will be determined by the workshop’s editorial team in accordance with the respective publishers’ requirements.

Authors must submit original, unpublished work that is not simultaneously under consideration elsewhere. All manuscripts must comply with the applicable submission guidelines, ethical standards, plagiarism policies, formatting requirements, and copyright procedures of the designated publisher.

Publication in either outlet is subject to the publisher’s final approval and completion of all required editorial and production procedures. Authors should consult the official publisher guidelines for applicable publication terms, fees, and other requirements.

Organizers