Workshops
ID 05 – Designing and Implementing AI in Healthcare: Experiences with Sociotechnical Approaches
Butler, Finkelstein, Matheny, Taber
AI-based tools may improve the precision and appropriateness of healthcare, ease the synthesis of complex information, and reduce the burden of clinical tasks. As with other clinical informatics interventions, the design and implementation of AI-based tools is likely to reconfigure a wide array of work practices, roles and responsibilities. Sociotechnical strategies for understanding clinical processes are vital to ensure that novel tools are responsive to the complex realities of healthcare. These complexities include factors like the tension between the team-based nature of clinical work and the
individualized nature of computer use; the need to navigate diverse information sources; uncertainty about the validity of clinical information; and often significant time pressure. Sociotechnical approaches support the development of AI-based tools that are transparent and trustworthy, and that augment, rather than disrupt, clinical practice by drawing on theories and methods from cognitive psychology, human factors engineering, anthropology and sociology, organization studies and other social sciences. A significant portion of current AI tool development focuses on diagnostic or other “traditional” clinical decision support use cases, with the promise of improved accuracy and precision over rule-based tools. Supported in part by large language models, tools beyond traditional point-of-care decision support have recently exploded, as well. These include applications like conversational agents for patient education and navigation, ambient transcription and rapid phenotyping in genetic testing pathways. Despite the proliferation of use cases, the empirical literature describing sociotechnical approaches to the design and implementation of AI-based tools remains limited. The goal of this workshop is to share real-world experiences with the design and implementation of AI-based tools in clinical settings.
ID 06 – Fostering collaboration between developers and physicians to optimize AI in healthcare
Postill, Rosella, Kimberly Lomis
The integration of Artificial Intelligence (AI) into healthcare is rapidly transforming medical practice. Successful deployment of AI models into healthcare requires interdisciplinary collaborations between clinicians and data scientists. Yet, such collaboration is impeded by the lack of shared understanding and language across disciplines. Additionally, there remains significant debate on the level of technical expertise required for healthcare professionals to
safely leverage AI tools. This workshop will explore the core technical AI competencies necessary for clinicians using AI, as well as the tenants of healthcare that data scientists need to be aware of when developing AI for healthcare and health research. Through interactive discussions, case studies, and hands-on activities, participants will evaluate different AI education models, discuss legal and ethical considerations, and propose strategies fostering AI literacy among healthcare professionals. The session will conclude with structured data collection to capture participants’ insights, informing a follow-up commentary on the evolving role of interdisciplinary collaboration and AI literacy in healthcare. To ensure the findings of this workshop translate to the broader community, our output will be a commentary summarizing the thoughts discussed in the workshop titled: The AI Learning Curve: What Physicians Need to Know About AI, and What Developers Need to Know About Medicine
ID 08 – Artificial Intelligence in Oncology
Uzun, Lussier
As advances in artificial intelligence (AI) continue to reshape the landscape of healthcare, this workshop aims to explore the transformative potential of AI in the field of cancer research and treatment. The integration of AI technologies presents unprecedented opportunities to enhance early detection, precision medicine, and personalized treatment
strategies for cancer patients. This session will delve into the machine learning (ML) based models identifying novel biomarkers, predicting cancer risk as well drug resistance using multi-modal data including EHR, multi-omics and imaging. By bringing together experts from both the AI and cancer research communities, this workshop aims to foster collaborative efforts, share insights, and pave the way for a future where AI plays a pivotal role in advancing cancer care.
ID 13 – From Bits to Qubits: Quantum Machine Learning for Medical Breakthroughs
Filippo Caruso, Sara Moccia, Simona Tiribelli, Mariachiara Di Cosmo
This workshop aims to explore the transformative potential of Quantum Machine Learning (QML) in healthcare.
We will discuss the advancements in hybrid classical-quantum algorithms, the challenges of integrating these technologies into clinical workflows, and the ethical considerations to tackle in order to ensure their responsible development and implementation.
The workshop will provide a platform for sharing innovative research, case studies, and fostering discussions on future prospects in the field of quantum-enhanced AI applications in healthcare.
ID 01 – AI in electrophysiology: Bridging Innovation and Clinical Practice
Faraci, Conte, Molinari
This workshop explores the role of AI in electrophysiology, focusing on diagnostics, patient monitoring, and personalized treatments through machine learning techniques. It will address AI applications such as arrhythmia detection and risk stratification while discussing key adoption challenges, including data limitations, clinical integration, and regulatory concerns. The event aims to bridge the gap between AI’s potential and clinical realities by highlighting emerging techniques like explainable AI and multimodal learning to drive innovation in the field.
ID 03 – AI and Microfluidics for Precision Medicine
Federica Caselli, David Dannhauser, Riccardo Reale
The workshop “AI and Microfluidics for Precision Medicine” is dedicated to exploring the integration of Artificial Intelligence (AI) techniques into microfluidic-based medical applications, enhancing their precision, efficiency, and scalability. The workshop is focused on leading-edge research and innovation and will feature research papers and panel discussions delving into key aspects of AI innovations and applications to microfluidic systems, including advanced diagnostics, personalized medicine, point-of care testing, drug discovery and delivery, and tissue engineering. The workshop will feature both innovative AI approaches as well as their applications to real-world problems. The workshop seeks to foster engagement of diverse stakeholders across healthcare research, including researchers, engineers, computer scientists, clinicians, and pharmaceutical professionals to promote multidisciplinary dialogue and collaboration. Attendees can anticipate interactive discussions, presentations, and networking opportunities, gaining valuable insights into the forefront of AI-driven strategies shaping the future of microfluidics for medical applications.
ID 11 – Explainable AI in Healthcare (XAI-Healthcare 2025)
G. Stiglic, J.M. Juarez, H. Zhengxing, P. Kocbek, D. Kim
The purpose of XAI-Healthcare 2025 event is to provide a place for intensive discussion on all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. This should result in cross-fertilization among research on Machine Learning, Decision Support Systems, Natural Language, Human-Computer Interaction, and Healthcare sciences. This meeting will also provide attendees with an opportunity to learn more on the progress of XAI in healthcare and to share their own perspectives. The panel discussion will provide participants with the insights on current developments and challenges from the researchers working in this fast-developing field.
Explainable AI (XAI) aims to address the problem of understanding how decisions are made by AI systems by designing formal methods and frameworks for easing their interpretation. The impact of AI in clinical settings and the trust placed in such systems by clinicians have been a growing concern related to the risk of introducing AI into the healthcare environment. XAI in healthcare is a multidisciplinary area addressing this challenge by combining AI technologies, cognitive modeling, healthcare science, ethical and legal issues.
ID 12 – Artificial Intelligence for Healthy Ageing: Advancing Prevention, Diagnosis, and Therapy of Chronic Degenerative Diseases (AIGE)
Ciro Mennella, Lucia Cascone, Umberto Maniscalco, and Massimo Esposito
The global healthcare system is facing a significant challenge due to the aging population and the increasing prevalence of chronic diseases. As these conditions become more widespread, there is an urgent need for innovative healthcare solutions that can improve prevention, diagnosis, and treatment. Artificial Intelligence (AI) offers a transformative approach to healthcare, enabling more effective prevention, management, and treatment strategies. By analyzing diverse data—medical records, genetics, lifestyle, and social determinants—AI promises earlier detection, diagnosis, and management of age-related diseases like neurodegenerative, cardiovascular, and musculoskeletal disorders.
This Workshop aims to facilitate a multidisciplinary dialogue, bringing together experts from healthcare, computer science, and engineering to explore the forefront of AI applications in age-related disease management. The Workshop will highlight emerging AI-driven innovations that improve healthcare outcomes and personalized therapies, while also addressing critical challenges, such as algorithmic bias, data privacy, scalability, and regulatory issues.
Through collaborative discussions and knowledge exchange, the Workshop aims to accelerate the responsible translation of AI into clinical practice, ultimately contributing to evidence-based solutions and methods to sustain more effective management of chronic diseases related to ageing.
ID 14 – Workshop in Memory of Mario Stefanelli
ID 17 – The 2025 International AI Applications in Public Health and Social Services (AI-PHSS 2025)
Huanmei Wu, Denis Newman-Griffis
The Workshop on AI Applications in Public Health and Social Services (AI-PHSS) explores how AI technologies enhance public health and social services, focusing on revolutionizing surveillance, tackling health issues, and informing policy decisions. It highlights AI’s potential to amplify community engagement, streamline social worker case management, and optimize service delivery. The workshop will bring together researchers, practitioners, healthcare professionals, and policymakers to share insights, exchange ideas, and foster collaborations in these critical areas. We invite submissions of papers and posters that investigate AI applications in public health and social services, such as predictive modeling for chronic disease management, real-world data analysis, sentiment analysis in social services, resource allocation optimization, and AI-driven decision support systems in public health and social services. Through interdisciplinary exchange, we aim to advance understanding of AI’s role in addressing public health challenges and improving social service provision efficiency. We are inviting original research submissions as well as work-in-progress to the Workshop on AI Applications in Public Health and Social Services (AI-PHSS).
ID 04 – SLM4Health: Improving Healthcare with Small Language Models
Denecke
SLM4Health focuses on exploring the role and potential of Small Language Models (SLMs) in healthcare-related natural language processing (NLP) tasks.
As SLMs gain traction in clinical settings due to their adaptability, efficiency, and lower resource demands, they offer a promising alternative to larger models, especially in resource-constrained environments. The workshop will address challenges such as performance trade-offs and ethical concerns including bias, privacy, and interpretability.
We aim to bring together researchers and practitioners to discuss SLM applications in clinical tasks, compare them with large language models, and explore methods to overcome these challenges, ultimately improving patient care and clinician support through more tailored NLP tools.
ID 07 – Fourth International Workshop on Artificial Intelligence in Nursing (AINurse-25)
Maxim Topaz, Charlene Ronquillio, Laura-Maria Peltonen,Lisianne Pruinelli, Martin Michalowski
Artificial intelligence (AI) is poised to revolutionize healthcare via data-driven solutions to improve patient outcomes. Nurses, the largest healthcare provider sector internationally, are rapidly adopting AI technologies in their everyday work. Existing nursing AI technologies help nurses identify patients at risk, assist in prioritizing nursing care, and improve nursing workflows. On the other hand, AI has the potential to introduce unintended consequences, including racial or other biases or erroneous care recommendations. AI technologies are being increasingly applied to data generated by nurses, and nurses are becoming one of the largest sectors in which AI is used in healthcare. This generates an increasing interest in the safe, ethical and clinically appropriate use of cutting-edge AI technologies in nursing.
This workshop, organized by the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, will focus on AI in nursing and provide a platform for discussing recent advances, cutting-edge AI methods, and charting a path forward for nursing AI. These goals will be achieved through a combination of presentation types, including paper presentations, invited talks, panels, demos, and general discussions. Intended workshop participants are individuals involved in developing and applying AI for nursing, including those with clinical (e.g., nursing, medicine), technical (e.g., machine learning, computer/data science) and human factors (e.g., visualization and UI/UX) backgrounds. AIME participants focusing on using AI technologies based on nursing data or intended to be used by nurses will benefit from this workshop by learning about current AI applications and cutting-edge methods. The workshop will also chart AI research areas that require further development to advance patient outcomes
ID 09 – Healthcare Empowerment and Advancement Leveraging xLM HEALxLM 2025
Aguzzi, Montagna, Dragoni
In recent years, the rapid advancements in xLMs have opened new possibilities across various industries, and healthcare is no exception. Organising a workshop on xLMs for healthcare is driven by several key motivations, specifically driven by the necessity to address critical challenges in healthcare with innovative solutions that can transform patient care and medical research.
One of the primary motivations is the potential for xLMs to enhance clinical decision support systems. With their ability to process vast amounts of medical literature, patient records, and multimodal clinical data, these models own the potential to support healthcare professionals, providing insights that improve diagnoses, treatment planning, and patient management. However, their effective integration into clinical practice requires careful evaluation for the solution to be trustworthy, making a dedicated workshop essential for discussing best practices, ethical considerations, and real-world implementation strategies. Another motivation behind this workshop is the opportunity to improve patient engagement and accessibility to healthcare. xLM-driven chatbots and virtual assistants can bridge gaps in communication, offering personalised health advice and answering patient queries, thus being a crucial component in telemedicine for supporting home-care delivery. The workshop would provide a platform for stakeholders–researchers, clinicians, and industry experts–to explore how these technologies can be designed responsibly, ensuring accuracy and safety. Finally, data privacy and bias mitigation are also central concerns that necessitate discussion. Given the sensitivity of medical data, deploying xLMs in healthcare requires robust frameworks for ensuring patient confidentiality and minimising algorithmic bias. The workshop would facilitate knowledge exchange on regulatory compliance, as well as fairness in AI-driven healthcare tools.
Accordingly, the event is meant to catalyse interdisciplinary collaboration. Healthcare professionals, AI researchers, policymakers, and industry leaders often operate in separate silos. Bringing them together in a focused workshop fosters dialogue, encourages partnerships, and accelerates the development of innovative solutions tailored to real-world healthcare needs.
ID 16 – 2nd International Workshop on Process Mining Applications for Healthcare
Fernandez-Llatas, Martin, Johnson, Sepúlveda, Munoz-Gama
The world’s most valuable resource is no longer oil, but data. The ultimate goal of data science techniques is not to collect more data, but to extract knowledge and valuable insights from existing data in various forms. To analyze and improve processes, event data is the main source of information. In recent years, a new discipline has emerged combining traditional process analysis and data-centric analysis: Process-Oriented Data Science (PODS). The interdisciplinary nature of this new research area has resulted in its application to analyze processes in a wide variety of domains. This workshop has an explicit focus on healthcare. The International Workshop on Process Mining Applications for Healthcare 2025 (PM4H25) provides a high-quality forum for interdisciplinary researchers and practitioners to exchange research findings and ideas on data-driven process analysis techniques and practices in healthcare. PM4H research includes a variety of topics ranging from process mining techniques adapted for healthcare processes, to practical issues related to the implementation of PM4H methodologies in healthcare organizations. During the 1st edition of our workshop at AIME, we aim to bring together researchers and practitioners in a spirit of collaboration and co-creation. In this way, we have the ambition to move PM4H research and practice forward, taking into account the distinguishing characteristics and challenges of the healthcare domain which were recently published in the Journal of Biomedical Informatics (https://doi.org/10.1016/j.jbi.2022.103994). This workshop is an initiative of the Process-Oriented Data Science for Healthcare Alliance, which is a chapter within the IEEE Task Force on Proce.
ID 10 – TruGen: Developing Trustworthy GenAI Virtual Assistants for Improved Healthcare Outcomes: A Human-Centered Explainable AI Perspective
Katrien Verbert, Robin De Croon, Cristina Conati, Gregor Štiglic
This workshop addresses the growing need for trustworthy and effective Generative AI (GenAI) solutions in healthcare, specifically focusing on the development of virtual assistants. We will explore how to design and implement GenAI-powered virtual assistants that improve healthcare outcomes while prioritizing human-centered design and explainability. Participants will gain a deep understanding of the key requirements for successful GenAI deployments in healthcare, including user-centricity, robustness, ethical considerations, and demonstrable clinical utility. The workshop’s objectives are to: (1) analyze the critical challenges and opportunities of GenAI in healthcare; (2) identify key technical, ethical, and practical challenges in healthcare GenAI; (3) apply human-centered design principles to ensure user acceptance and real-world impact; (4) delve into explainable AI (XAI) techniques to foster transparency and trust; (5) brainstorm innovative use cases; (6) build connections among those interested in related research; and (7) initiate the development of concrete project ideas. Through paper presentations, interactive sessions, and collaborative discussions, attendees will gain valuable insights and practical skills, with a strong emphasis on human-centered and explainable AI methodologies, to contribute to the development of responsible and effective GenAI solutions for healthcare.