Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence: Realizing Precision Medicine One Patient at a Time
Reporter: Stephen J Williams, PhD @StephenJWillia2
The impact of Machine Learning (ML) and Artificial Intelligence (AI) during the last decade has been tremendous. With the rise of infobesity, ML/AI is evolving to an essential capability to help mine the sheer volume of patient genomics, omics, sensor/wearables and real-world data, and unravel the knot of healthcare’s most complex questions.
Despite the advancements in technology, organizations struggle to prioritize and implement ML/AI to achieve the anticipated value, whilst managing the disruption that comes with it. In this session, panelists will discuss ML/AI implementation and adoption strategies that work. Panelists will draw upon their experiences as they share their success stories, discuss how to implement digital diagnostics, track disease progression and treatment, and increase commercial value and ROI compared against traditional approaches.
- most of trials which are done are still in training AI/ML algorithms with training data sets. The best results however have been about 80% accuracy in training sets. Needs to improve
- All data sets can be biased. For example a professor was looking at heartrate using a IR detector on a wearable but it wound up that different types of skin would generate a different signal to the detector so training sets maybe population biases (you are getting data from one group)
- clinical grade equipment actually haven’t been trained on a large set like commercial versions of wearables, Commercial grade is tested on a larger study population. This can affect the AI/ML algorithms.
- Regulations: The regulatory bodies responsible is up to debate. Whether FDA or FTC is responsible for AI/ML in healtcare and healthcare tech and IT is not fully decided yet. We don’t have the guidances for these new technologies
- some rules: never use your own encryption always use industry standards especially when getting personal data from wearables. One hospital corrupted their system because their computer system was not up to date and could not protect against a virus transmitted by a wearable.
- pharma companies understand they need to increase value of their products so very interested in how AI/ML can be used.
Please follow LIVE on TWITTER using the following @ handles and # hashtags:
@Handles
# Hashtags
#BIO2019 (official meeting hashtag)
Leave a Reply