Details
During the past decades, there are lots of exciting artificial intelligence (AI) based innovations that drive healthcare revolution. U.S. health care spent about $3.5 trillion (19% of US GDP) in 2017 with a 25% increase within five years from $2.8 trillion in 2012. Plenty of efforts have been taken to reduce the costs in the healthcare systems, among which we have seen tremendous growth opportunities in AI application to healthcare. With increasingly available health and genomic data, AI-driven technologies are enabling innovations in advancing medical knowledge discovery and health care delivery. AI can effectively reduce healthcare labor cost as well as improve efficiency in image analysis, administrative workflow optimization, virtual nursing assistants and dosage error reduction.
However, AI healthcare practical implementation is still facing multiple challenges: Current regulation lacks of standards to assess AI system safety and efficacy; The continuation of the data exchange might become an obstacle for sustaining AI system as current healthcare infrastructure doesn’t provide incentives for data sharing. Although many AI applications have been helping clinicians with productivity, clinical decision making is still rare. Overcoming those problems would require close collaboration between healthcare and technology community.
This conference will bring together an illustrious group of healthcare practitioners, life science researchers, data scientists, technologists and entrepreneurs to share their in-depth knowledge, and how data science and AI technologies drive improving patients’ health care quality, boosting system efficiency, reducing costs, and so on.
AGENDA1:00 - 1:30 Registration
1:30 - 1:40 NECINA Introduction - Fan Wu
1:40 - 2:00 Dennis Grishin, Cofounders, Nebula Genomics. Forbes Healthcare 30 under 30
2:00 - 2:20 Da Zhang, Medical Imaging Physicist at BIDMC, Assistant Professor of Radiology Harvard Medical School - Data Driven CT Radiation Dose Monitoring and Imaging Protocol Management – Introduction from a data science perspective
2:20 - 2:40 Cao (Danica) Xiao, Research Staff Member, IBM Research
3:00 - 3:20 Zhang Chen, PhD, Data Scientist, Dana-Farber Cancer Institute - Applications of Deep Learning in Biomedical Research and Healthcare
3:20 - 3:40 Jixin Cheng, Moustakas Professor (ECE, BME), Boston University - Deep-learning Chemical Microscopy
3:40 - 4:00 Panel Discussion
4:00 - 4:40 Startup Showcase
Ji-Xin Cheng
Moustakas Professor (ECE, BME), Boston University
Professor Cheng is currently Moustakas Professor (ECE, BME) at Boston University. He and his team have been constantly at the most forefront of the rising field of label-free optical spectroscopic imaging in technology, science, and clinical translation. His research has been supported by over 25 million ($) fund from federal agencies and private foundations. Cheng is authored in over 220 peer-reviewed articles with an h-index of 69 (Google Scholar). He organized/chaired 26 national/ international symposia, delivered over 200 invited talks. He holds 10 US patents for the invention of CARS microscope and other technologies. He is the lead editor of the first book on coherent Raman scattering microscopy, CRC Press, 2012. In 2014 He co-founded Vibronix Inc which has the mission of saving lives through medical device innovations.
Professor Cheng has been recognized by Fellow of Optical Society of America (2018), SPIE Translational Research Awards (2018, 2016, 2014), Purdue University College of Engineering Research Excellence Award (2016), Craver Award from Coblentz Society (2015), Chang-Jiang Scholar from Chinese Minister of Education (2015), Fellow of American Institute of Medicine and Biological Engineering (2014), Purdue University Faculty Scholar (2012-17), College of Engineering Early Career Research Award (2011), Research Excellence Award from Purdue Center for Cancer Research (2011), Outstanding Young Scientist Award from Chinese National Academy of Sciences (2009).
Dennis Grishin
Cofounders, Nebula Genomics. Forbes Healthcare 30 under 30
Dennis Grishin is the co-founder and Chief Scientific Officer at Nebula Genomics where he is building a distributed genomic data generation, sharing and analysis platform. Dennis is also a computer scientist with a Master’s degree from Harvard University and a Boehringer Ingelheim Ph.D. Fellow in Genetics and Genomics at Harvard Medical School.
Cao (Danica) Xiao
Research Staff Member, IBM Research
Cao (Danica) Xiao is a Research Staff Member in the AI for Healthcare Team of IBM Research AI, located in Cambridge, Massachusetts. Before joining IBM, she got her Ph.D. degree from University of Washington, Seattle in 2016. Her thesis focused on machine learning and data mining with medical and healthcare applications. During her PhD training, she also worked as research intern at Group Health Research Institute in 2013, data scientist intern at LinkedIn from 2014 to 2015, and research intern at IBM Research in 2016, respectively.
Her research interests focus on developing machine learning and deep learning models to solve real world healthcare challenges. Particularly, she is interested in deep computational phenotyping, adverse drug reaction signal detection from heterogeneous real world evidence, causal inference from observational data, and translational informatics research (e.g., drug similarity for drug safety and discovery).
Da Zhang
Medical Imaging Physicist at BIDMC, Assistant Professor of Radiology Harvard Medical School
Dr. Zhang obtained his PhD from University of Oklahoma. He is now an assistant professor at of Radiology at Harvard Medical School and a medical imaging physicist at Beth Israel Deaconess Medical Center. His research interest: Informatics in Radiology, Machine Learning, Data Science, Medical Imaging Physics, Radiation Dose and Imaging Performance Monitoring and Management.
Zhang Chen
Data Scientist, Dana-Farber Cancer Institute
Dr. Chen obtained his PhD from University of Pennsylvania and worked at Thomas Jefferson Hospital as a data scientist. His research is mainly focused on developing statistical and machine learning models for biomedical data analysis. His current work involves analyzing genomics, EHR, image, clinical text data as well as hospital operational data to improve healthcare in the hospital.
Start-up Showcase
There would be several digital health domain startup showcase. Each start-up company would have 7 minutes for presentation and 3 minutes for Q&A.
Nebula Genomics
Founded by George Church, Dennis Grishin, Kamal Obbad, Nebula Genomics leverages blockchain technology to eliminate middlemen and empower people to own their personal genomic data.
VibroniX
Vibronix, Inc. is a medical device company that aims to provide precise disease diagnosis and treatment. Powered by its tech engine in advanced bio-photonics, Vibronix now has three technology platforms: Coherent Raman Scattering Imaging, Photoacoustic Imaging and Optical Sensing. Vibronix’s latest product: CARSA-Coherent Anti-stokes Raman Scattering imaging for Antibiotic Susceptibility test will be presented for the first time. Antimicrobial resistance causes nearly 1 million deaths from drug-resistant infections each year globally. If no action taken, over 10 million deaths and $100 trillion GDP loss will be expected by 2050. To combat this crisis, we develop CARSA to shorten the current 2-days antibiotics susceptibility test into 10 minutes to gain precious time to save more lives. To date, Vibronix has received 4 STTR/SBIR grants from NIH and NSF.
VitaData
VitaData, a decentralized health data exchange powered by blockchain. The VitaData way is collaboration with best-in-class technology providers and resellers so as to bring data to life for the communities we serve.
ChaveTech 智医惠民
Commonwealth & Huntington Ave. Crop.
ChaveTech team was formed since August 4th, 2017 in Boston. We are a team that focuses on use Internet Technology as leverage to help improving Chinese family doctors (primary care) market; we are aiming to take the responsibility to help transport the limited Chinese medical resources into the grass-root level of China (rural area or small cities which lack sufficient medical resources); we are trying to apply global version to our project and relocate international resources back to the Chinese market or vice versa.