Technion-Rambam Initiative in Medical AI

Launching event of TERA

About Us

The Technion-Rambam Initiative in Medical AI, “TERA” was launched in March 2022 as a joint-initiative between The Technion and Rambam Health Care Campus – combining clinical expertise, basic science, and engineering in fighting human disease using large medical datasets and state-of-the-art advances in AI.
The mission of TERA is to initiate, support and promote academic research and educational activities between the two institutions and partners in the field of medical AI.

In the growing field of AI in medicine, the current situation is that solutions and insights developed in the lab usually do not enter the clinical practice. TERA aims to tackle this gap between research studies and the clinical implementation and health benefit that AI can provide.

Rambam collects medical data and biological samples as part of routine practice. Furthermore, clinical experts in Rambam come up with problems and challenges that, if solved, could significantly improve the care patients receive. The combination of health and biobank data with Rambam’s clinical knowledge is fertile ground for Technion scientists who can create and prospectively evaluate new algorithmic solutions to the most pressing medical challenges in diverse fields ranging from internal medicine to cardiology, cancer and intensive care to name a few.

TERA is uniquely poised to develop AI based solutions and rapidly translate them to a clinical-study in real-world settings aimed to assess their use and effectiveness in improving patient outcomes. Therefore, TERA aims to close the loop between medical data and biological sample availability and their actionable benefit to patient care. Research projects aim to improve clinical practices, quality of care, decisions on treatment strategies and medication selection, effectively implementing personalized medicine. We believe we are uniquely positioned to create a generator for innovation and discovery on the basis of the complementarity of Technion faculties and Rambam clinical expertise.

TERA includes a dedicated ecosystem and physical space integrated in Rambam. The physical space is the meeting point between researchers, clinician scientists, and graduate and MD/PhD students. It is staffed with dedicated professionals in the fields of data-science, IT, clinical trial methodologist, epidemiologist and statistician. TERA also addresses the ethical challenges in medical data sharing by providing the necessary ecosystem to work on the data “on site” or via secured cloud technology. Accordingly, TERA intention is to create an effective pipeline to properly collect, prepare, validate, document and share the large streams of medical data being generated in the dozens of medical units. TERA also support the regulatory and technical work needed to deploy pilot systems within Rambam units.

TERA Programs

Seminar Series

The monthly seminar provides a unique opportunity to listen to leading engineers and clinicians working in the field of medical data science. The guest talks will introduce to a wide audience the field of clinical data science, including those with both technical and non-technical backgrounds and provide a perspective on its future impact to the field of medicine. Register here to the TERA mailing list to stay tuned about future events.

MedAI Course for Healthcare Professionals

This course for medical doctors and healthcare professionals aims to introduce the foundational principles of Machine Learning and Artificial Intelligence (ML/AI) and their applications in healthcare and natural sciences to healthcare professionals. Attendees do not need to have prior experience in coding. The goal is to empower attendees and provide a basis for understanding the transformative role AI can play in data analysis and management both for research and daily practice.

2024 MedAI course for Healthcare Professionals (closed)

BSc Research Projects in Medical AI

This initiative aims to bridge the gap between academia and clinical practice by providing students with the opportunity to work on real-world medical data challenges under the guidance of data scientists and experienced health professionals. This program aims to bring relevant projects and provide support to those selected projects while these be run in the regular setting of their home faculties.

2024 TERA BSc Research Projects in Medical AI (closed).

Seed Funds

Seed Funds are provided by TERA through competitive internal calls with the intention to initiate new research projects between principal investigators at Technion and Rambam. The seed fund should enable the researchers to obtain sufficient results to publish or apply to competitive grants.

2 023 1st Round Call – Machine learning in medical AI (closed)

Research Space

A dedicated space for collaborative research in medical AI between the Technion and Rambam was just inaugurated and now available for use. The space is located at the Meyer Building (building 5), 4th floor. The space can occupy up to six investigators on a “warm seat” basis for staff and Technion students involved in TERA projects that wish to work on-site on clinical data and interact with their clinical collaborators.

Projects

CardioAI:

Intelligent monitoring for the robust diagnosis of cardiovascular diseases using continuous long term ECG recordings.
People: Joachim A. Behar (Technion), Mahmoud Suleiman (Rambam)

ICU’s predictive tools:

Developing advanced tools to track and predict deterioration of critically-ill patients in the intensive care unit (surveillance and clinical decision support including treatment).
People: Danny Eytan (Rambam), Ronit Almog (Rambam), Joachim A. Behar (Technion)

Cardio – Kidney failure:

Individual-level causal inference for prevention and optimal care of acutely decompensated heart failure patients developing acute kidney injury.
People: Uri Shalit (Technion), Oren Caspi (Rambam)

ICU’s decision making tools:

Fusing mechanistic and data-driven models for decision making in dynamic environments (real-time information on the patient’s cardiovascular status, expected trajectory and underlying disease processes).
People: Danny Eytan (Rambam), Shie Mannor (Technion), Uri Shalit (Technion)

BMT-BSI PredictIT:

Establishing a predictive model for blood stream infections (BSI) during bone marrow transplantation (BMT).
People: Ivan Gur (Rambam)

Birth prediction:

Developing advanced tools to track and predict birth weight.
People: Ron Beloosesky (Rambam)

Pancreatic Cancer:

NLP Generated High Complexity Combination Therapy for Pancreatic Cancer.
People: Dr. Yosi Shamay (Technion) and Prof. Irit Ben Aharon (Rambam)

Nutrition:

Utilizing artificial intelligence for nutritional intake monitoring in hospitalized patients can harmonize nutritional care
People: RN Irena Papier (Rambam), Dr. Dvir Aran (Technion), Dr. Shay Perek (Rambam), Dr. Yona Vaisbuch (Rambam), RN Gila Hyams (Rambam), Dr. Haggai Bar-Yoseph (Rambam)

Kinematics:

A Wearable Kinematics and Machine learning Approach for Rebalancing Knee Loads
People: Dr. Arielle Fischer (Technion), Dr. Kfir Yehuda Levy (Technion), Dr. Bezalel Peskin (Rambam), Dr. Nabil Ghrayeb (Rambam)

Suicide Prevention:

Advancing Research and Practice in Suicide Prevention Through Artificial Intelligence
People: Prof. Roi Reichart (Technion) and Prof. Eyal Fruchter (Rambam)