esantus
AI Expert & Human Computation Advocate
ai in healthcare
AI is revolutionizing the health care pipeline
We partner with hospitals and research institutes (e.g. MGH, Dana Farber and others)
We are developing information extraction models from medical reports and images
We will exploit the extracted information to produce population-based studies, provide risk assessments and support clinical decisions
Related websites:
It is just matter of a time before Artificial Intelligence will revolutionize the heathcare pipeline. From risk assessment to disease diagnosis, from care delivery to administrative tasks, AI is set to boost healthcare efficiency, radically improving its quality and drastically reducing its costs. When machines will be able to automatically assess patient needs and smartly allocate resources, patients will enjoy shorter waiting lists and receive more personalized care.
In the United States, the Center for Data Innovation has already called for a national strategy to guide the AI explosion and its application to healthcare. Similar initiatives are currently happening all over the world.
In our project, we will focus on various types of cancer and heart failure data. We will cooperate closely with hospitals and research institutes (e.g. MGH, Dana Farber, and others) to develop models for the automatic extraction of information from medical reports and images. Such information will be then provided in a structured form to be exploited for population-based studies, and for supporting risk assessment and clinical decisions.
collaborators
regina barzilay
delta electronic professor, eecs, mit
adam yala
phd candidate, eecs, mit
kevin s. hughes
breast cancer surgeon, mgh
charlotta lindvall
medical instructor, dana farber
amir tahmasebi
senior research scientist, philips
susu yan
medical physicist, mgh
zhijing jin
undergraduate student, hku
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RT @agingdoc1: Towards AI-driven longevity research: An overview @nikmarino9, @EnricoSantus & coauthors including @biogerontology… https://t.co/PMCjIUsKdz -
After over a year, my personal website is finally updated! Go to give a look and let me know what you think in the… https://t.co/un8LLZy9Dt
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Come to meet us at @hcomp_conf or ping us in the chat! We are pushing forward #HCOMP #HIL https://t.co/TtxvmbcW6r -
RT @PolyUCBS: Congratulations to #PolyUCBS PhD graduate Dr. Enrico Santus for being awarded the Outstanding Young Alumni Award in… https://t.co/vgvSFnGJ8T
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RT @Culturesrl: Così @EnricoSantus, esperto AI, durante la prima tappa del Tavolo #DatieIntelligenzaArtificiale. La prossima tappa… https://t.co/YQHaxHU3mO
Because of the high cost of turning free-text medical reports to structured data, a large number of investigations in the clinical literature are nowadays based on small subsets of the population that is actually affected by a disease. This limits the possibility of identifying complex and fine-grained patterns from the data, substantially reducing our ability of understanding of the studied problem…