Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medical EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.
Medial EarlySign's CMO, Dr. Jeremy Orr, will be speaking at a session entitled "Analyte to Algorithm to Action to Impact" at G2 Intelligence's 37th Annual Lab Institute. Machine learning models are beginning to play a critical role in care transformation efforts, especially as they relate to risk adjustment, population health, and alternative payment models. Dr. Orr will review the development and validation of several powerful models to predict high burden disease at earlier, more treatable stages. He will further review the challenge of getting these predictors into clinical and laboratory workflow, how ML results are messaged to both patients and clinicians, and how clinical outcomes related to these predictors are measured. Dr. Orr will close with a look ahead to how ML models and approaches will improve care delivery for laboratorians and other clinicians, and how ML empowers the transition to value based care.
Medial EarlySign are a leader in machine-learning based solutions in the early detection and prevention of high burden diseases using AI technology to detect the early warning signals and health risks associated with major diseases. The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in the Journal of the American College of Cardiology: "Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analysed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals. "Contemporary risk models have traditionally had little success in identifying patients' post-PCI risks for complications, in-patient mortality, and hospital readmission. This study shows that machine learning tools may enable cardiology care teams to identify patients who may be on high-risk trajectories," said Rajiv Gulati, MD, Ph.D., Interventional Cardiologist at Mayo Clinic.