DeveloperWeek Seattle: Cloud Edition

I will discuss both the current state of research in Ai as well as some of the ethical and regulatory challenges that we are currently facing in the medical field.  It is estimated that data in healthcare will double every 73 days, however much of this data is lost in silos, and research and innovation stunted by poor interoperability as well as data harmonization abilities. Although we have made advances in being able to personalize medicine, image recognition, and identify target genes for novel therapies, improvements in the ability to validate these topics, lack of discourse between datascientists and physicians, and important ramifications of algorithms (e.g influencing someone's life or well being) is necessary for the field to advance. Additionally, our work has demonstrated that most ML algorithms do not perform better than logistic regression, which has been used by the medical field for decades, raising the question of what additive benefits in many clinical scenarios ML may have. Finally, I urge individuals to consider what we are optimizing (length of life? quality of life?) and how some of these metrics may be measured or achieved.