This Artificial Intelligence was 92% Accurate in Breast Cancer Detection Contest


A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed a way to train artificial intelligence to read and interpret pathology images. Scientists tested the artificial intelligence (AI) during a competition at the annual International Symposium of Biomedical Imaging, where it was tasked to look for breast cancer in images of lymph nodes. It turns out it can detect breast cancer accurately 92 percent of the time and won in two separate categories during the contest. Andrew Beck from BIDMC says they used the deep learning method, which is commonly used to train AI to recognize speech, images and objects. They fed the machine with hundreds of slides marked to indicate which parts have cancerous cells and which have normal ones.

Artificial Intelligence and Risk Communication

AAAI Conferences

The challenges of effective health risk communication are well known. This paper provides pointers to the health communication literature that discuss these problems. Tailoring printed information, visual displays, and interactive multimedia have been proposed in the health communication literature as promising approaches. On-line risk communication applications are increasing on the internet. However, potential effectiveness of applications using conventional computer technology is limited. We propose that use of artificial intelligence, building upon research in Intelligent Tutoring Systems, might be able to overcome these limitations.

Minimal Sufficient Explanations for Factored Markov Decision Processes

AAAI Conferences

Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for course-advising through a user study involving students.