Patient Centered Decision Intelligence 

Every patient is unique. Harness Sparkdit’s decision intelligence to make the right decisions for every patient

Sparkdit stands out by bringing decision intelligence to healthcare through patient-centered decision aids. Combine expert knowledge, patient feelings, and tradeoffs to generate individualized treatment plans aligned with their care goals.

Better Recommendations. Better Treatments. Better Outcomes.

Biopharma: Molecular Early Selection

Augment AI with Sparkdit Decision Intelligence to find the next great cure.

Finding the next great therapeutic is always a challenge. By Augmenting AI with Decision Intelligence Spakdit can optimize the selection of the right molecule, reduce risk, and help teams collaborate by deciding what’s essential to drive research and business success.

Better Deicisions. Better Investments. Better Cures.

Virtual Care

Healthcare anywhere, anytime.  Harness Sparkdit’s decision intelligence to deliver care intelligently and immediately

Sparkdit creates condition-specific virtual visits that patients can access anytime and anywhere.  Sparkdit’s decision intelligence engine helps patients and providers make and act on care decisions immediately.

Better Recommendations. Better Treatments. Better Outcomes.

Reducing False Positives Predictions

Causality solution to address false positive challenge by reverse engineering AI predictions

In early detection, when predicting a health conditions based on statistical analysis, such as AI-based models, the tolerance to false positives is very low. Imagine the devastating impact of informing someone that they may have a high propensity for a certain medical condition when in fact they do not. Imagine their life, their stress in the subsequent weeks and months as they go through a battery of test and medical consultations only to discover that it was a false alarm. 

The results of Neural Network contain on average between 10% and 38% of false positives. That is simply unacceptable in healthcare.

The reason for the false positives is by and large the fact that AI acts like a black box. And thus causality is a problem. There is no explanation to a given score or classification. (other than this element meets the patterns of other elements that were labeled with the positive outcome label).

Sparkdit offers an innovative solution to validate the outcome of an AI model. Sparkdit's Inference Engine can reverse engineer the process to either justify or invalidate an AI prediction. The elements that are determined to have a reasonable explanation for the outcome and removed from the false positive list. This process significantly reduces the false positive list.

Sparkdit's Inference Engine validates the output of the AI System (typically the propensity  to contract a certain health condition) by (i) capturing expert knowledge at scale using generative AI (ii) Patient data, lifestyle, trends, history, health data, behavior and patterns.

By trying to explain what combination of factors contributed to the positive outcome of the AI model, our customer are reducing their false positives by 50%