Special Session 1
Transforming Data into Actionable Knowledge:
AI and Retrieval for Intelligent Health Systems
This special session focuses on how artificial intelligence (AI), natural language processing (NLP),
and information retrieval can transform large volumes of healthcare data into actionable knowledge.
Healthcare systems generate vast and complex data from electronic health records, clinical notes,
imaging, and digital platforms — but much of it remains fragmented and unstructured, limiting its
use in clinical decision-making.
Rationale & Motivation
Why this session matters
- Extract meaningful insights from unstructured healthcare data
- Improve clinical decision-making through AI-driven systems
- Tackle critical challenges: data interoperability, reliability, transparency, and governance
- Ensure trustworthy and safe AI deployment in real-world healthcare environments
💡 The session integrates AI with structured knowledge systems to power intelligent, data-driven healthcare — from clinical documentation to decision support.
Scope & Key Topics
01AI for healthcare knowledge extraction
02Information retrieval from clinical text and electronic health records
03Large language models (LLMs) for clinical applications
04Automated clinical coding and terminology mapping
05AI-assisted clinical documentation (e.g., voice-to-EMR systems)
06Data standardization and interoperability
07Retrieval-augmented generation and knowledge-grounded AI
08AI-driven clinical decision support systems
09Trustworthy and safe AI in healthcare
10Implementation challenges in low- and middle-income countries
Expected Audience & Impact
Who Should Attend
- Researchers in AI, data science, and health informatics
- Clinicians and healthcare practitioners
- Healthcare administrators and policymakers
- Computer scientists working on NLP and information retrieval
Session Impact
- Encourages interdisciplinary collaboration across AI and healthcare
- Promotes practical, deployable AI solutions for health systems
- Advances clinical decisions, healcare management, and public health outcomes
Submission & Publication
Submission Requirements
- Original, unpublished research in English
- 10–15 pages following Springer template
- Similarity score < 15%
- AI-generated content score < 20%
- No simultaneous submissions to other venues
- Must adhere to Springer Publishing Policies
Publication
- Published in Springer CCIS series
- Scopus indexed proceedings
- Full peer review per CCIS standards
- Presentation at conference required for inclusion
- Subject to publisher guidelines & indexing requirements
🔗 Submit your papers electronically via the EDAS portal, under the track entitled "SS1-AI and Retrieval for Intelligent Health Systems"
EDAS
Deadline
Submission Deadline
30 April 2026
If you require visa letters, accessibility accommodations, or special session guidance, mention it in your email for faster routing.
Special Session Organizers
Session Organizer · 01
Dr. Annisa Ristya Rahmanti
Faculty of Medicine, Public Health and Nursing
Universitas Gadjah Mada, Indonesia
annisaristya@ugm.ac.id
Universitas Gadjah Mada, Indonesia
Brief Bio
Dr. Rahmanti is a health informatics researcher specializing in artificial intelligence, clinical decision support systems, and digital health transformation. She has been working extensively in artificial intelligence for healthcare, specializing in natural language processing (NLP) and large language models (LLMs). She has developed award-winning chatbots for weight management and suicide prevention, recognized in the TMU x MIT Hackathon competitions. She currently serves as an Assistant Professor in Health Informatics at the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.
She was also a British Council Women in STEM Fellow at Middlesex University, London, UK, where she worked on developing an LLM for oesophageal cancer that integrates educational content with endoscopic images. Her current research focuses on AI-enabled digital twin technologies for stroke prediction and prevention, as well as ambient clinical intelligence to support automated clinical documentation and decision-making in healthcare systems.
Dr. Rahmanti is a health informatics researcher specializing in artificial intelligence, clinical decision support systems, and digital health transformation. She has been working extensively in artificial intelligence for healthcare, specializing in natural language processing (NLP) and large language models (LLMs). She has developed award-winning chatbots for weight management and suicide prevention, recognized in the TMU x MIT Hackathon competitions. She currently serves as an Assistant Professor in Health Informatics at the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.
She was also a British Council Women in STEM Fellow at Middlesex University, London, UK, where she worked on developing an LLM for oesophageal cancer that integrates educational content with endoscopic images. Her current research focuses on AI-enabled digital twin technologies for stroke prediction and prevention, as well as ambient clinical intelligence to support automated clinical documentation and decision-making in healthcare systems.
Session Organizer · 02
Prof. Ts. Dr. Nurfadhlina Mohd Sharef
Faculty of Computer Science and Information Technology
Universiti Putra Malaysia
nurfadhlina@upm.edu.my
Universiti Putra Malaysia
Brief Bio
Prof. Ts. Dr. Nurfadhlina Mohd Sharef is a professor of computer science and Chief Digital Officer at Universiti Putra Malaysia. She has extensive experience working on artificial intelligence and data science, particularly in adaptive AI and intelligent data analytics. Her current research focuses on digital twin technologies, deep reinforcement learning, and deep neural network architectures for multi-tasking, multi-criteria, and multi-objective optimization.
She has also been actively involved in projects related to learning analytics, personalized learning, and active learning in digital education environments. In addition, she has led consultancy projects applying data science solutions for public and private sector organizations.
Prof. Ts. Dr. Nurfadhlina Mohd Sharef is a professor of computer science and Chief Digital Officer at Universiti Putra Malaysia. She has extensive experience working on artificial intelligence and data science, particularly in adaptive AI and intelligent data analytics. Her current research focuses on digital twin technologies, deep reinforcement learning, and deep neural network architectures for multi-tasking, multi-criteria, and multi-objective optimization.
She has also been actively involved in projects related to learning analytics, personalized learning, and active learning in digital education environments. In addition, she has led consultancy projects applying data science solutions for public and private sector organizations.
Person to Contact
For inquiries regarding this special session, please contact:
Dr. Annisa Ristya Rahmanti
Universitas Gadjah Mada, Indonesia
annisaristya@ugm.ac.id
