Aim

In recent years, the development of AI systems aimed at supporting clinical decisions has greatly expanded, employing a wide range of techniques, such as machine learning and deep learning. AI systems are used for a variety of applications, such as diagnosis, personalized medicine, triage assessment, disease prevention and monitoring, and drug development. However, the biomedical field is also a sector in which complaints, litigation, criminal charges for medical malpractice, and administrative fines are increasing worldwide, therefore special attention should be paid to legal issues that may arise from the use of AI systems. AI systems may have a significant legal impact on multiple stakeholders: researchers, producers, health care professionals, patients, and society as a whole. From a legal point of view, the whole AI life cycle generates the need for regulatory and ethical compliance. Because of the fact that health data (considered special categories of data according to art. 9 of GDPR) is involved, and that the AI Act requires many new obligations for medical devices, discussing regulatory compliance is crucial. In addition, due to the known issues regarding discrimination and biases in health care AI, an ethical assessment is also extremely important. At a global level, there is a call for the development of Trustworthy AI systems, which is particularly important in the biomedical field. Some scholars have even pushed the debate further, advocating for explainability and interpretability for all systems employed in the healthcare sector, thus considering black-box approaches not compliant with ethical and legal standards. This Special Session aims at gathering scholars and industry actors who are investigating new directions and ideas in the field of AI and Law & AI Ethics, in particular in the biomedical field.

Scope

This Special Session welcomes papers regarding any legal issue (including privacy) and ethical aspects of AI systems; in particular, we are interested in Automated Decision-Making systems (ADM), applied in the context of the biological or medical domain. We expect to have a minimum of three speakers (30 min each plus introduction and closing remarks, about two hours in total).

Examples of topics include but are not limited to:

Explainable/Interpretable AI systems for medical decision support

Right of explanation and Trustworthy AI in the biomedical field

Biases in AI systems and debiasing techniques

Fair methods for data preprocessing

Unbiased data augmentation and privacy-preserving generative models

Anonymization and pseudonymization of biobanks

How personalized medicine through AI systems can contribute to mitigating discrimination and inequalities in healthcare

Privacy aspects of telemedicine

The use of synthetic data to protect personal data

Legal and ethical issues of data collection, data cleaning, feature/variable selection, and other phases of the development

Ethical framework for medical AI systems

Ownership of patients’ data and conditions for reuse

Legal consequences of a biased AI model

Medical device regulation and AI

AI regulation proposal and AI models in biomedical research

Legal and ethical issues concerning the use of smart robots for surgery

Civil liability derived from Automated Decision Making systems in medical practice

Legal aspects of machine learning applied to medical imaging

Legal issues of open-source AI systems

Sustainable AI & environmental issues

Critical Data Theories regarding AI in the biomedical field

AI and the European Health Data Space

Data Governance Act and Health Data Altruism for AI

Comparative perspectives on the above issues in different legal systems (papers exploring the legal system of under-represented countries are very welcome)

Chairs

Chiara Gallese (University of Turin, Italy)

Elena Falletti (Carlo Cattaneo University LIUC, Italy)

Daniele Papetti (University of Milano-Bicocca, Italy)

E-mail: chiara.gallese@unito.it

Aim

The special session aims to integrate advanced electronics, artificial intelligence (AI), and IoT technologies to enhance efficiency, precision, and functionality in various industries. These systems are designed to automate processes, monitor performance in real-time, and optimize energy consumption. They are widely utilized in manufacturing, healthcare, and smart home applications, providing innovative solutions to modern challenges. Intelligent visual inspection combines AI, machine learning, and computer vision to identify defects, assess quality, and ensure compliance in production lines. This technology employs high-resolution cameras and sophisticated algorithms to analyze images or video feeds, enabling rapid and accurate detection of irregularities. Its applications include electronics manufacturing, automotive assembly, and food safety, significantly improving quality control while reducing human error and costs.

Chairs

Chun-Hung Yang(National Formosa University, Taiwan)

Wen-Ho Juang(National Formosa University, Taiwan)

Shih-Yu Chen,(National Yunlin University of Science and Technology, Taiwan)

Yung-Ming Kuo(National Formosa University, Taiwan)

E-mail: ymkuo@gs.nfu.edu.tw

Aim

With the rapid advancement of technology, medical devices are undergoing an unprecedented revolution. This integration of AI, IoT, and big data has given rise to a new generation of smart medical devices, providing more precise, efficient, and personalized solutions for diagnosis, treatment, and prevention. This special session will focus on the innovative design and clinical application of smart medical devices, including topics such as design and development, clinical applications, telehealth, and home care applications. This session will provide a cross-disciplinary platform for sharing research findings, clinical experiences, and industry trends. Promoting exchanges and collaborations, this session aligns with the CIBCB 2025 themes, highlighting future challenges and opportunities for medical devices.

Scope

We invite submissions addressing legal and ethical topics related to AI systems, especially their applications in biomedical or healthcare fields.

Examples include but are not limited to:

• AI-Powered Diagnostics: This topic could explore how artificial intelligence is being used to improve the accuracy and speed of medical diagnoses.
• Wearable Sensors for Continuous Monitoring: This topic could delve into the latest advancements in wearable sensor technology for continuous health monitoring.
• IoT-Enabled Medical Devices for Connected Care:  This topic could focus on how the IoT is transforming medical devices and healthcare delivery.
• Big Data Analytics for Personalized Medicine: This topic could examine how big data analytics is being used to tailor medical treatments to individual patients.
• Human-Robot Collaboration in Healthcare: This topic could explore the growing role of robots in healthcare, particularly in assisting with surgery, rehabilitation, and patient care.

Chairs

Yi-Chun Du (NCKU, Taiwan)

David T.W. Lin (National Uniersity of Tainan, Taiwan)

Chun-Ping Jen (National Chung Cheng University, Taiwan)

E-mail: terrydu@gs.ncku.edu.tw

Aim

The special session on computational intelligence for brain-computer interfaces is to convene experts and researchers to apply computational intelligence (CI) technologies from the basics of neural networks, computational intelligence, and evolutionary computation to brain-computer interfaces. CI serves as a pivotal core technology within AI, contributing significantly to the development of intelligent systems, including algorithms, multilayer perceptron, and cognitive developmental systems. Encompassing various themes, this session delves into CI in biomedical engineering, innovative healthcare applications, and signal processing within the context of brain-computer interfaces.

Scope

his session serves as a platform for exchanging cutting-edge research, theories, and methodologies pertinent to the dynamic field of brain-computer interfaces. Attendees can benefit from exposure to diverse perspectives, novel algorithms, and empirical studies that shed light on the latest advancements in computational intelligence techniques applied to enhance human-computer or human-machine collaboration and interface. Additionally, engaging in such sessions fosters networking opportunities, enabling researchers to establish collaborations, share insights, and potentially spark interdisciplinary initiatives that further propel innovation in this field. Overall, this special session expects the impact of computational intelligence on researchers who are willing to deepen their skills in computer science, mathematics, electrical engineering, robotics, brain-computer interfaces, and related areas.

Examples include but are not limited to:

• Computational Intelligence for Biomedical Engineering
• Computational Intelligence for Biomedical Image Processing
• Computational Intelligence for Signal Processing
• Computational Intelligence for Brain-Computer Interfaces
• Computational Intelligence for Brain-Machine Interaction
• Computational Intelligence for Human Cognition
• Computational Intelligence for Innovative Healthcare Applications
• Inspiring Computational Intelligence for Co-learning or Innovative Fuzzy Systems Developments

Chairs

Li-Wei Ko (National Yang Ming Chiao Tung University, Taiwan)

Cong-Ying He (National Yang Ming Chiao Tung University, Taiwan)

E-mail: lwko@nycu.edu.tw

Aim

Recent advancements in deep learning and wearable sensor technologies have paved the way for new methods in biomedical signal processing. This special session aims to bring together researchers working on novel approaches to sleep apnea detection using ECG signals, focusing on time-frequency transformation and transformer-based models. The session will also cover real-time implementation, dataset challenges, and regulatory considerations for wearable medical devices.

Scope

We anticipate receiving 8-12 paper submissions from leading research groups and industry experts working on biomedical signal processing and wearable healthcare technologies. 

Examples include but are not limited to

• ECG-based sleep apnea detection
• Transformer models in biomedical signal analysis
• Real-time signal processing for wearable medical devices
• AI-driven diagnostic tools for sleep disorders 

Chairs

Kang-Ping Lin (Chung Yuan Christian University, Taiwan)

Chong-Yao Su (Kaohsiung Medical University Chung-Ho Memorial Hospital, Taiwan)

Cheng-Wen Yan (National Sun Yat-Sen University, Taiwan)

Cheng-Yu Lin (National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Taiwan)

You-Liang Xie (Hon Hai Precision Industry Co., Ltd, Taiwan)

Febryan Setiawan National Cheng Kung University, Taiwan)

E-mail: kplin@cycu.edu.tw