HACARUS founding mission was to "Expand Human Life to 120 Years", at the time through a dietary guidance app, with AI and related hardware - following successful rollout of its small data centric AI core, the technology has been used to solve problems in a wide range of fields, all the while keeping true to a core focus on taping into tacit knowledge.
Today HACARUS has pivoted its mission to "Bringing the next generation of quantification to all Industries” to reflect our broad appeal.
Now we are looking to bringing experience from digitizing expertise from manufacturing and infrastructure industries back to medical and life science, by creating new instruments that allow medical professionals to lead the way towards as better future.
Quote：the Broad Bioimage Benchmark Collection Ljosa et al., Nature Methods, 2012
Sparse modeling is HACARUS' proprietary AI technology that does not require big data. Because it is effective and has excellent interpretability and explainability even with a small amount of data, it has recently been introduced in the medical field, where it is difficult to collect a large amount of personal data.
Today HACARUS' supports diagnosis and tests based on image analysis performed by medical professionals and promotes digital transformation at medical sites by improving efficiency and reducing burden. Image analysis using AI technology can be applied in a wide variety of fields, such as early detection and diagnosis of illnesses in medical fields, automating quality inspection, and monitoring of manufacturing processes. HACARUS' aims for innovative changes in medical and drug discovery areas by making full use of image analysis technology utilizing sparse modeling.
Deep learning requires a huge amount of data to create accurate models, and the decision making process is a Blackbox, making it unsuitable to the medical field where transparency and understanding the rationale behind decisions is key, and the availability of data often is scarce. HACARUS' is pioneering Sparse Modeling, which excels with small data, and offers excellent interpretation making it a natural fit for implementation of AI in the medical and life science fields.
Large data sets is the foundation of conventional AI, however medical illness data is scarce, and comes with personal information issues, even in cases when anonymized, making it difficult to collect large datasets. For rare conditions, this problem is even greater. HACARUS' novel approach based on Sparse modeling is designed to remedy these issues, starting from a small data focused approach, and can achieve high accuracy results even with less data.
While AI can support in analysis, the final decision is taken by medical professionals. Therefore, it is vital that the decision-making process AI takes is transparent and understandable to human operators. Hacarus' Sparse Modeling is Whitebox AI, meaning that the reasoning behind a conclusion suggested by AI is clearly outlined.