Exploring clinical decision making
About Us
DataMED Lab at Tel Aviv University explores clinical decision-making through the lens of artificial intelligence and data science to bridge the gap between computational methods and the intuitive needs of healthcare professionals.
We analyze large-scale clinical datasets and integrate advanced computational methods:
including machine learning, deep learning, and natural language processing with both real-world medical records and simulated clinical environments.
This hybrid approach enables us to study clinical decision-making, optimize care workflows, and design AI-powered tools that directly support healthcare professionals and improve system efficiency.
Among our projects: Developing a dynamic risk-stratification algorithm using Hebrew NLP and ML for emergency and community settings, Modeling clinical reasoning patterns through a conceptual framework inspired by behavioral analysis, Evaluating how large language models (LLMs) and generative AI can augment clinical decision support, Piloting INTEGRA: an interactive triage system based on generative reasoning analytics.
Our international collaborations include: The EU JUST CT initiative (ESR iGuide) on imaging equity and safety, Remote stroke diagnosis through AI-driven platforms, Identifying women at high risk of breast cancer using NLP-based approaches, And building dynamic algorithms for patient prioritization in resource-constrained environments.
Through these diverse projects, we aim to bridge clinical insight with data-driven innovation; translating ideas into impactful tools that improve healthcare delivery.
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Our PhD Students
Our MSc Students
MSc student
Shaked Mondani Dollev
AI in the ED: Effects on Patient Safety and Quality of Care.
MSc student
Mahmoud Hamdan
GenAI to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format
MSc student
Natalia Weiner
Integrating overseas nursing program graduates into Israeli healthcare institutions: Challenges, barriers and opportunities
MSc Graduate
Nitzan Shasoua
Primary Care nurses' Decision-Making Process for Referrals to Urgent Hospital-Based Services
MSc Graduate
Nofar Mizan
The effect of Covid-19 pandemic on emergency nursing care
MSc student
Dana Balog
The Mediating Role of Initial Nursing Anamnesis in Triage in the Gap Between Reported Pain at Admission and Discharge from the ED
MSc student
Dani Greenberg
Understanding Reasons for Returning to the ED After Urgent Care Clinic Discharge Without Referral
MSc student
Sharinne Herzig
The association between training based on personalized videos and self-efficiency and hospital revisits among patients who underwent stoma surgery
MSc Graduate
Shahed Abomukh
Effect of Covid-19 pandemic on Body Mass Index, diabetes control and healthcare services utilization in children and adolescents diagnosed with type 1 diabetes
MSc Graduate
Anagam Kitani
The effect of dialysis quality on patient satisfaction and dropout from care in a Hemodialysis center in Taibe
MSc student
Michal Berkovich
Interaction between human and machine: Examining the degree of compatibility between the recommendations of an AI system with a medium level of autonomy and the medical team’s decisions in the decision-making process in the ED.
MSc student
Dr. Lior Moskovich
Utilizing AI in Medical Imaging Tests: Mapping Current Applications, Team Attitudes, and Predicting its Impact on Radiological Workforce
MSc student
Reshef Peled
Integrating overseas nursing program graduates into Israeli healthcare institutions: Challenges, barriers and opportunities
MSc Graduate
Matan Torjeman
The association between body image and self-esteem and a healthy lifestyle among postpartum women: a mixed method study
MSc Graduate
Katia Shlonsky
Examining the Utility of AI as a Decision Support Tool in Clinical Simulations among Registered Nurses
Publications
Recent publications (selected)
Hack, S., Attal, R., Elazar, D., Alon, Y., Meyuchas, R., Livne, L., Madgar, O., & Saban, M. Cautionary Lessons from Real-World Testing of GPT-4.1 AI for Pediatric Foreign Body Aspiration. (2025). European Archives of Oto-Rhino-Laryngology
Singer, C., Saban, M., Luxenburg, O., Yellin, L. B., Hierath, M., Sosna, J., ... & Brkljačić, B. (2025). Computed tomography referral guidelines adherence in Europe: insights from a seven-country audit. European Radiology, 35(3), 1166-1177.
Sosna, J., Joskowicz, L., & Saban, M. (2025). Navigating the AI Landscape in Medical Imaging: A Critical Analysis of Technologies, Implementation, and Implications. Radiology, 315(3), e240982.
Shanwetter Levit, N., & Saban, M. (2025). When investigator meets LLM: A qualitative analysis of cancer patient decision-making journeys. NPJ Digital Medicine.
Levin, C., Zaboli, A., Turcato, G., & Saban, M. (2025). Nursing judgment in the age of generative artificial intelligence: A cross-national study on clinical decision-making performance among emergency nurses. International Journal of Nursing Studies, 105216.
Orkaby, B., Kerner, E., Saban, M., & Levin, C. (2025). Bridging generational gaps in medication safety: insights from nurses, students, and generative AI models. BMC nursing, 24(1), 382.
Saban, M., Alon, Y., Luxenburg, O., Singer, C., Hierath, M., Karoussou Schreiner, A., & Sosna, J. (2025). Comparison of CT referral justification using clinical decision support and large language models in a large European cohort. European Radiology, 1-10.
ISO 690
Alon, Y., Naimi, E., Levin, C., Videl, H., & Saban, M. (2025). Leveraging natural language processing to elucidate real-world clinical decision-making paradigms: A proof of concept study. Journal of Biomedical Informatics
Levin, C., Orkaby, B., Kerner, E., & Saban, M. (2025). Can large language models assist with pediatric dosing accuracy?. Pediatric Research
Saban, M., Lutski, M., Zucker, I., Uziel, M., Ben-Moshe, D., Israel, A., ... & Merzon, E. (2025). Identifying diabetes related-complications in a real-world free-text electronic medical records in Hebrew using natural language processing techniques. Journal of diabetes science and technology, 19(4), 999-1007.
Saad O, Saban M, Kerner E, Levin C. (2025) Augmenting Community Nursing Practice With Generative AI: A Formative Study of Diagnostic Synergies Using Simulation-Based Clinical Cases. Journal of Primary Care & Community Health.
Events
Lecture at the Engineering-Academic Knowledge Fund Seminar
The talk focused on one of the most urgent questions in modern healthcare:
How can we integrate artificial intelligence into clinical decision-making without losing the human element - the patient, the caregiver, and the connection between them?
It explored the opportunities presented by generative AI, the ethical dilemmas it raises, and the critical decision points where intuition, empathy, and clinical experience remain irreplaceable.
Presentation at ESICM 2024, Barcelona
As part of the ESICM 2024 conference in Barcelona, the Head of our Lab presented a talk titled:
"Human–GenAI Partnerships: Comparing ICU Nurse Triage and AI in Critical Care."
The lecture offered new insights into how generative AI can enhance the clinical expertise of ICU nurses. It explored the evolving relationship between human judgment and AI capabilities, highlighting the potential for collaboration -
not replacement - in the future of critical care.
Synergy Conference on BioImaging & AI
This groundbreaking event brought together leading experts in bioimaging and artificial intelligence for a day of interdisciplinary dialogue and innovation.
Organized by the Head of DataMED Lab at Tel Aviv University, the conference hosted over 100 participants — including radiologists, radiographers, health policymakers, and academic researchers — to explore new frontiers at the intersection of technology and clinical imaging.
Highlights from the Synergy Conference
The conference featured keynote talks by leading experts, including Prof. Rachel Miron on national wait-time measurement, Dr. Gad Levi on the future of radiology, and Prof. Jacob Sosna on implementing comprehensive AI solutions in healthcare.
Participants praised the event for fostering meaningful knowledge exchange in medical imaging and artificial intelligence. The discussions emphasized the importance of interdisciplinary collaboration and advanced technologies in improving patient care — setting the stage for future partnerships in this critical field.
Collaborations
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Contact Us
Interested in working with us?
Whether you're a researcher, student, clinician, or industry partner - we’re always open to meaningful collaborations.
If you have an idea, a question, or a project you'd like to explore together, don’t hesitate to reach out.
We believe that innovation in healthcare starts with a good conversation.
Gray Faculty of Medical and Health Sciences,
Tel Aviv University