Artificial intelligence (AI) has great potential to transform many aspects of health care – but despite this promise has only been implemented in a limited number of settings and in a limited way. This talk begins by discussing the potential of AI to transform health care in areas such as disease diagnosis and treatment, disease monitoring, patient monitoring and clinical outcome prediction, health care operations, and health care administration. We then outline the many reasons why this potential has not been realized. As a microcosm illustrating the state of AI applications, we describe a number of projects carried out at Lucile Packard Hospital Stanford. We conclude with thoughts about what is needed in order for AI to be successfully integrated into health care systems and the next steps that can be taken to advance implementation.

Margaret L. Brandeau is Coleman F. Fung Professor of Engineering and Professor of Health Policy (by Courtesy) at Stanford University. Her research focuses on the development of applied mathematical and economic models to support health policy decisions. Her recent work has examined HIV and drug abuse prevention and treatment programs, programs to control the opioid epidemic, and COVID-19 response strategies. She is an INFORMS Fellow. From INFORMS, she has received the Philip McCord Morse Lectureship Award, the President’s Award, the Pierskalla Prize (twice), and the Award for the Advancement of Women in Operations Research and the Management Sciences. At Stanford she has received the Eugene L. Grant Faculty Teaching Award from the School of Engineering and the Graduate Teaching Award from the Department of Management Science and Engineering. Professor Brandeau earned a BS in Mathematics and an MS in Operations Research from MIT, and a PhD in Engineering-Economic Systems from Stanford.

Our bodies are built out of billions of cells, each containing DNA and RNA with information on our health and disease states. AI has increased in popularity as a useful approach to solving many complex bio-computational challenges. We show that we can employ advanced bioinformatics in order to identify early changes that can lead to diseases. Our work will lead to the seamless integration of bio-digital breakthroughs in medicine.

Professor Noam Shomron is passionate about using basic science to advance better healthcare. Professor Shomron heads the Functional Genomic Team at the Faculty of Medicine at Tel Aviv University after training at MIT. He leads a multidisciplinary team of scientists that develops computational methods for parsing big data in the bio-medical field using Artificial Intelligence. Shomron applied for more than 30 patents and published more than 200 peer-reviewed publications on multiple genomic fields, including medicine, agriculture, and business. Shomron also leads the Tel Aviv University Innovation Lab (TILabs) on Digital Medicine. He is the Editor of ‘Deep Sequencing Data Analysis’ book (Springer, Edition I 2013, and II 2021); Director of ‘Rare-Genomics’ Israel (NPO); Academic Director of ‘ScienceAbroad’ (NPO); Co-founder of Variantyx, which provides clinical whole genome sequencing services; Co-founder of GotSho, a software platform for genetic and biomedical labs; Co-founder of IdentifAI that establishes noninvasive prenatal diagnostics at a single nucleotide resolution from a first-trimester blood test.

This presentation delves into the field of speech analysis, focusing on the topic of attacks on voice conversion models. I will discuss the current state of the art in voice conversion technology and the various challenges faced by these models. I will also provide insight on how to create effective online voice conversion model.

Dr. Nitzan Shahar is the head of the Clinical Computational Psychology Lab at Sagol School of Neuroscience and School of Psychology, Tel-Aviv university. His work is aimed at using computational modeling to describe latent cognitive processes underlying attention-deficits/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD), in children and adults. Specifically, his studies uses mostly theoretical modeling including evidence accumulation and reinforcement-learning in humans. He completed three years of post-doctoral studies at the UCL computational psychiatry center in London. Finally, Dr. Shahar is also a practicing clinical psychologist and holds a one-day clinic, aimed at helping children and young adults with ADHD/OCD. 

Elad Mandelbaum joined Elbit Systems - EW & SIGINIT - Elisra in 2010 as an Algorithm Engineer. From 2010 to 2021, Elad worked in Elisra's Computer Vision R&D group and helped design the algorithms for Elisra's IR-based missile warning systems. Since 2021, he has been working in the AI R&D group at Elisra. Elad holds a master's degree in applied mathematics and a bachelor degree in physics and mathematics from Tel Aviv University.

Migraine is one of the most common neurological disorders, affecting over 1 billion people worldwide [1], ranked second among the causes of disability globally [2] and there are no clinical biomarkers for migraine attacks [3]. While the term migraine is usually associated with the few hours of the pain phase, the physical and clinical changes associated with the migraine phenomena may start a few days prior to the pain phase and may last a few days afterward, therefore, can be divided into 3 stages [3]; (1) The migraine prodrome: Premonitory symptoms that occur before the headache phase, (2) The headache phase: lasts 4-72 hours, (3) The migraine postdrome: Residual symptoms after the pain phase that may last up to 72 hours.Understanding migraine attack phases can help anticipate and manage the symptoms. Therefore, our aim is to demonstrate the ability to distinguish between the different migraine stages using personal physiological metrics based on continuously monitoring by smartwatches. Using data collected continuously from smart watches and cellular phones, we can identify precursors and conditions that are correlated with different stages of migraine attacks.
 

Contributors: Moria Dagan, Adar Frenkel, Aviv Balassiano, Tamar Spector, Gad Resef, and Eddie Aronovich 

Co-Founder and CTO of Indolex, a medical AI company that focuses on AI using continuous monitoring of patient data. Eddie Aronovich has over 20 years of experience as a technology leader. Previous to Indolex, Eddie established the data science activity at Ultra-Cruize, a GM autonomous driving project, and led the Tel-Aviv University, Computer Science Lab. He earned his MSc and B.Sc. in statistics and operation research from         Tel Aviv University. He served as a board member in various professional organizations: ISA, AEAI, ISOC-IL, and Derech ha-Yeled Montessori school.

The recent advancement in deep learning has re-opened the field of acoustics sensing for unique defense related
tasks such as drone detection, gunshot detection, ultrasonic sensing and more. Learn about the recent advances in this field and how the IAI team uses deep learning to overcome the past limitations of sound sensing to build innovative new products.

Development team leader at ELTA. Founded the IAI acoustics group in IAI's innovation center and leading the system design and algorithm development for all acoustics products. Author of several patents In the field of defense acoustics. An expert in the field of signal intelligence (SIGINT) data fusion systems, also implementing ML in these systems since 2018. Has a monthly column in Hebrew on innovative AI technology.