It is no secret that artificial intelligence is expected to have a ground breaking impact in the healthcare sector. From chronic diseases and cancer to radiology and risk assessment, there are endless opportunities to leverage machine learning algorithms to deploy more precise, efficient, and impactful interventions at exactly the right moment for a patient.


Next, we list our solutions for Healthcare enterprises.


  • Save human lives.
  • Reduce operative costs by 5%-15%.
  • Save Doctor's time.

Medical Imaging

Intelligent medical imaging can analyze and identify diseases images to support the health professional decision-making process. Examples of this solutions can be using images to characterize the phenotypes and genetic properties of tumors; or collecting images to classify skin lesions, wounds or infections.


  • Cost savings.
  • Faster diagnosis.
  • Save Doctor's time.

Automatic Diagnosis with Bots

Automated agents can suggest the best treatment based on the patient historical data and put in place mechanisms to detect and prevent possible diagnosis errors. Bots can be specially useful in cases where the illness is low risk allowing the health professionals to focus on more important or urgent cases.


  • Reduce number of visit to the hospital.
  • Improve patient treatment.
  • Improve workflow efficiency.

Health monitoring with IoT telemetry

Whether you are a doctor, a nurse or a recovering patient, Internet of Things (IoT) solutions provide endless opportunities for real-time health monitoring. Smart wearables can transmit securely and effectively patient status, data and other pertinent health information to the staff in emergency situations.


  • Save costs by 20%-50%.
  • Save human lives.

Early diagnosis

Late diagnosis of treatable illnesses is one of the biggest causes of avoidable deaths. The use of Big Data and AI for early detection and diagnosis could fundamentally transform outcomes for people with different chronic diseases, such as heart diseases, prostate cancer or lung cancer, as well as saving hospitals money.


  • Reduce operative costs by 5%-20%.
  • Save Doctor's time.
  • Improve patient drug response.

Personalized medication & care

There is a high variability in patient drug response due to genetic factors, age, nutrition habits, health status and environment conditions. Machine learning can be used to analyze vast amounts of data and provide insights to help the doctor provide optimal diagnostics and treatments.


  • Reduce operative costs.
  • Reduce device down time.
  • Reduce risk of device failing during a medical procedure.

Predictive maintenance of devices

Having a medical device fail in an operating room can lead to complications due to prolonged anaesthesia, risk of infection or wasted time in inconvenient replacement of the device. Monitoring the life-cycle and being able to anticipate impeding failures of the device provide a competitive advantage to medical instrumentation manufacturers. AI can address this problem by monitoring internet connected devices and performing predictive maintenance of those that are in sub-optimal state to schedule maintenance requests and prevent undesirable device down time.

Do you have a use case for your industry that is not listed here? Send us an email to