[AI Development Case] Virginia Corporation

Clarifying annotation standards! Introducing a case where we successfully improved accuracy without increasing the amount of data.

We would like to introduce a case study of a Proof of Concept (PoC) for diagnostic support AI that assists with diagnostic discrepancies in a beauty clinic for Virginia Corporation. The company was considering incorporating diagnostic support AI into their existing system, but they were unable to achieve practical accuracy through in-house development. Therefore, they defined requirements based on the actual counseling flow and successfully built an AI that meets the needs of the field. To properly train the AI on diagnoses that can lead to differing judgments, they conducted multiple interviews to redefine annotation standards that could be understood even by non-medical professionals. As a result, they succeeded in improving the accuracy of the AI model without increasing the amount of data. [Case Overview] ■ Challenges - They want to incorporate diagnostic support AI into their cloud business support system in the future. - They want to support the diagnostic discrepancies arising from the experience of physicians. - They could not achieve practical accuracy through in-house development. *For more details, please download the PDF or feel free to contact us.

Other Software