There has been a huge amount of media interest in Artificial Intelligence and its applications and outcomes in healthcare, particularly over the past few weeks. While the technology has yet to establish unquestionable credibility there is a growing number of promising use cases for the technology in the healthcare space. This week, we have rounded up some of the most promising AI offerings and highlighted some interesting outcomes to date.
#1 Streamlining Clinical Trial Recruitment
Finding patients for clinical trials is notoriously laborious and time-consuming. AI can mine large amounts of clinical patient data, structured and unstructured, to find matches to eligibility criteria. Clinithink and Deep6 are forerunners in our community working with providers to solve these issues
Clinithink are helping trial coordinators find 90 more patients in one-eighth of the time as reported in their Case Study with Mount Sinai...Read More
#2 Drug Discovery & Identifying Biomarkers
In addition to clinical trial recruitment AI is offering a promising opportunity for pharma to analyse genetic information on patients in order to understand what treatments work best for different segments of the population.
These technologies can analyse vast amounts of data, making connections for biomarker discovery and drug repurposing. Deep Genomics and Linguamatics are two interesting examples of companies leading the way for evidence based solutions in this space.
#3 Diagnostics and Clinical Decision Support
Startups and other innovators in the ecosystem are now attempting to aggregate previously disparate information sources including medical images, patient-reported data, lab results, EHR data and genomic information to build diagnostic solutions. Enlitic and MedyMatch are two to watch.
Every time a doctor sees a patient, they are solving a complex data problem. The goal of each case is to arrive at an optimal treatment decision based on many forms of clinical information, such as the patient’s history, symptoms, lab tests, and medical images. The quality and quantity of this data is rapidly improving—it’s estimated to grow over 50-fold this decade, to 25,000 petabytes worldwide by 2020.
#4 Patient Engagement and Stratification
Chatbots are generally consumer solutions, front line hospital systems and remote monitoring solutions that converse with patients gathering information and stratifying patients often alerting HCP of high-risk patients. Check out three interesting examples of companies in the community Sense.ly, Babylon and Conversa, who recently closed their Series A with Northwell Ventures.
Explore Big Data and AI at the #HXLGG Dublin
Big Data and AI will be one of the main focus areas of our Dublin Global Gathering this September where we will be hosting an invite only working group discussion with a handful of thought leaders in the space to explore points like that raised by Dr. Ethan Weiss below. You can apply to join the selected 100 digital health thought leaders here
If there anyone in your network you think we should be speaking to about Big Data and AI in healthcare feel free to send on their details or make the connection directly at firstname.lastname@example.org - I'd love to hear your insight!
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