posted by user: JunlinHou || 1045 views || tracked by 3 users: [display]

TAI4H 2024 : IJCAI 2024 The Second International Workshop on Trustworthy Artificial Intelligence for Healthcare

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/view/tai4h2024
 
When Aug 4, 2024 - Aug 4, 2024
Where Jeju, Korea
Submission Deadline May 17, 2024
Notification Due Jun 4, 2024
Final Version Due Jun 15, 2024
Categories    trustworthy   AI   healthcare
 

Call For Papers

Artificial intelligence (AI) has achieved or even exceeded human performance in many healthcare tasks, owing to the fast development of AI techniques and the growing scale of medical data. However, AI techniques are still far from being widely applied in healthcare practice. Real-world scenarios are far more complex, and AI is often faced with challenges in its credibility such as lack of explainability, generalization, fairness, privacy, etc. The development of trustworthy artificial intelligence for healthcare (TAI4H) is hence of great importance to enhance the trust and confidence of doctors and patients in using the related techniques. We aim to bring together researchers from interdisciplinary fields, including but not limited to machine learning, clinical research, and medical imaging, etc., to provide different perspectives on how to develop trustworthy AI algorithms to accelerate the landing of AI in healthcare.

Interested topics will include, but not be limited to:
Generalization to out-of-distribution samples.
Explainability of machine learning models in healthcare.
Reasoning, intervening, or causal inference.
Debiasing AI models from learning from shortcuts.
Fairness in medical imaging.
Uncertainty estimation of machine learning models and medical data.
Privacy-preserving AI for medical data.
Learning informative and discriminative features under weak annotations.
Human-machine cooperation (human-in-the-loop, active learning, etc.) in healthcare, such as medical image analysis.
Multi-modal fusion and learning, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, pathology, genetics, electronical healthcare records, etc.
Adversarial attack and defence in healthcare.
Benchmarks that quantify the trustworthiness of AI models in medical imaging tasks.
Foundation model pre-training and adaptation.

The goal of this TAI4H workshop is to bring together expertise from academia, clinic, and industry with an insightful vision of promoting trustworthy artificial intelligence for healthcare in terms of scalability, accountability, and explainability. The challenges to AI come from diverse perspectives in practice, and it is therefore of great importance to establish such an interdisciplinary platform to encourage sharing and discussion of ideas, implementation, data, labelling, benchmarks, experience, etc, and jointly advance the frontiers of trustworthy AI for healthcare.

Related Resources

RTEE 2024   10th International Conference on Recent Trends in Electrical Engineering
ICSTTE 2025   2025 3rd International Conference on SmartRail, Traffic and Transportation Engineering (ICSTTE 2025)
SPT 2024   International Conference on Signal Processing Trends
21st AIAI 2025   21st (AIAI) Artificial Intelligence Applications and Innovations
ICAART 2025   17th International Conference on Agents and Artificial Intelligence
CMVIT-Maldives 2025   2025 9th International Conference on Machine Vision and Information Technology (CMVIT 2025)
AIMDS 2024   International Conference on AI, Machine Learning and Data Science
AMLDS 2025   IEEE--2025 International Conference on Advanced Machine Learning and Data Science
SIGEM 2024   10th International Conference on Signal, Image Processing and Embedded Systems
25th EANN/EAAAI 2025   25th (EANN/EAAAI) Engineering Applications and Advances of of Artificial Intelligence