K Health offers free, personalized and user-friendly health guidance. With just a few clicks, you'll be able to learn how people like you were diagnosed and treated by real physicians.
The K application was developed by K Health using the unique physician-based dataset from KSM & Maccabi. By mining the anonymized free-text dataset with millions of doctor notes from patient-doctor visits in Maccabi, K was able to create sophisticated machine learning models that find people who are similar to each and every user. K continues to learn from ongoing interactions between users and Maccabi physicians - making sure every user gets the best possible medical information.
Ibex Medical Analytics
Ibex Medical Analytics pioneers AI-based cancer diagnostics in pathology. Through building a partnership with KSM, Ibex uses advanced artificial intelligence and machine learning technologies to develop clinical grade algorithms and workflows that identify cancer as accurately as a human pathologist.
Ibex's solutions help pathologists and providers improve cancer diagnoses, efficiencies and health economics by increasing accuracy, enabling 100% quality control, reducing turnaround times and operating costs.
In a joint research by KSM and Vocalis on over 10,000 chronic patients, Vocalis developed a non-invasive vocal biomarker that successfully predicted readmission and mortality.
The study and its results were published in the journal of American Heart Association in April 2018.
KSM and Zebra-Med developed a joint project to create a large-scale clinical validation mechanism for Zebra-Med's mammography product - HealthMammo, with the goal of improving the mammography screening operations at Assuta Medical Centers. Zebra-Med’s artificial intelligence solutions analyze millions of clinical images in real time, empowering radiologists and helping healthcare providers manage the ever increasing workload without compromising quality.
Researchers from KSM along with their counterparts at IBM Research - Haifa are using image analysis technologies and deep learning techniques to ‘teach’ computers how to identify breast cancer findings in mammography images.
The ability for machines to detect breast cancer from mammography images can be used at first to assist doctors in decision-making. Later on, the vision is to combine this technology with additional patient data to create a predictive analysis algorithm that could actually help prevent breast cancer.