DR. Talia Tron

Realizing AI potential in healthcare – challenges & solutions

DR. Talia Tron

DR. Talia Tron

Realizing AI potential in healthcare – challenges & solutions

DR. Talia Tron

Bio

Talia is a data scientist and an innovation catalyst at Intuit, a leading fin-tech company, and a senior lecturer at the Digital Medical Technologies program in HIT. She holds a Ph.D. in computational neuroscience from the Hebrew University, in which she developed automatic tools for analyzing non-verbal behavior in schizophrenia. Before her work at Intuit, she worked as a data scientist in Microsoft in the security and education domain.

Bio

Talia is a data scientist and an innovation catalyst at Intuit, a leading fin-tech company, and a senior lecturer at the Digital Medical Technologies program in HIT. She holds a Ph.D. in computational neuroscience from the Hebrew University, in which she developed automatic tools for analyzing non-verbal behavior in schizophrenia. Before her work at Intuit, she worked as a data scientist in Microsoft in the security and education domain.

Abstract

The healthcare industry is generating approximately 30% of the world’s data volume. With the velocity and variety of this data, the potential of AI to transform the healthcare domain is overwhelming – from pandemic outbreak prediction, automatic diagnosis, drug discovery to personalized treatment – the possible benefits to healthcare institutes, clinicians, and patients cannot be stressed enough. Nonetheless, unlike other domains, where the usage of AI is becoming increasingly popular, the field of medicine seems to be left behind. In this round table, we will dive into some of the unique challenges that need to be addressed for AI and machine learning to be fully embraced and integrated within healthcare systems. These challenges include the huge disparity between clinicians’ and patients’ understanding and ML applications, the fragmentation of clinical data in multiple channels and data sources, and the absence of ground truth to train ML algorithms. In addition, the heavy regulation in this domain leads to unique data security and governance requirements and strict processes for bias mitigation and monitoring. After mapping the relevant challenges, we will focus on the main factors that could influence the trust of clinicians and patients in AI systems in the healthcare domain and what we can do as data scientists to improve this trust. Resources: – Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of medical Internet research, 22(6), e15154 – Panesar, Arjun. Machine learning and AI for healthcare. Coventry, UK: Apress, 2019.

Abstract

The healthcare industry is generating approximately 30% of the world’s data volume. With the velocity and variety of this data, the potential of AI to transform the healthcare domain is overwhelming – from pandemic outbreak prediction, automatic diagnosis, drug discovery to personalized treatment – the possible benefits to healthcare institutes, clinicians, and patients cannot be stressed enough. Nonetheless, unlike other domains, where the usage of AI is becoming increasingly popular, the field of medicine seems to be left behind. In this round table, we will dive into some of the unique challenges that need to be addressed for AI and machine learning to be fully embraced and integrated within healthcare systems. These challenges include the huge disparity between clinicians’ and patients’ understanding and ML applications, the fragmentation of clinical data in multiple channels and data sources, and the absence of ground truth to train ML algorithms. In addition, the heavy regulation in this domain leads to unique data security and governance requirements and strict processes for bias mitigation and monitoring. After mapping the relevant challenges, we will focus on the main factors that could influence the trust of clinicians and patients in AI systems in the healthcare domain and what we can do as data scientists to improve this trust. Resources: – Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of medical Internet research, 22(6), e15154 – Panesar, Arjun. Machine learning and AI for healthcare. Coventry, UK: Apress, 2019.

Planned Agenda

8:45 Reception
9:30 Opening words by WiDS TLV ambassadors Or Basson and Noah Eyal Altman
9:40 Dr. Kira Radinski - Learning to predict the future of healthcare
10:10 Prof. Yonina Eldar - Model-Based Deep Learning: Applications to Imaging and Communications
10:40 Break
10:50 Lightning talks
12:20 Lunch & Poster session
13:20 Roundtable session & Poster session
14:05 Roundtable closure
14:20 Break
14:30 Dr. Anna Levant - 3D Metrology: Seeing the Unseen
15:00 Aviv Ben-Arie - Counterfactual Explanations: The Future of Explainable AI?
15:30 Closing remarks
15:40 End