Medical AI Data Processing Anonymization and Standardization
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Solution Overview
Problem
Existing techniques for processing medical data for AI and drug discovery are inefficient in utilizing data effectively.
Innovation Solution
An information processing method that involves acquiring anonymized image and clinical data from medical devices, selecting relevant data, associating disease and organ information, and standardizing the data for use in medical AI and drug discovery AI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple image information and clinical information are acquired and processed through anonymization, selection, association, and standardization, then the quality and usability of data for medical AI/drug discovery AI is improved, but the complexity of the data processing system increases
Solution Approach 1:
The data processing system is divided into distinct functional modules: an anonymization processing unit that removes private information from image and clinical data, a selection unit that identifies target data suitable for medical AI, an association unit that links disease and organ information, and a standardization unit that ensures data consistency. This segmentation allows each module to handle specific tasks independently, improving data quality while managing system complexity through modular architecture.
2Productivity
If comprehensive data processing including anonymization, selection, association, and standardization is performed, then the efficiency of data utilization in medical AI is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs anonymization processing on image and clinical information before selection and association operations. By removing private information in advance, the system prepares data in a standardized format that facilitates subsequent processing steps. This preliminary action reduces the computational burden during later stages, improving overall data utilization efficiency while managing processing time through proactive data preparation.
3Object-affected harmful factors
If private information is anonymized in image and clinical data, then patient privacy protection is improved, but the amount of usable information for research decreases
Solution Approach 1:
The anonymization processing unit extracts and removes only the private information components from image and clinical data, while preserving the medically relevant information. This selective extraction approach allows the system to protect patient privacy by removing identifiers and sensitive personal information, while maintaining the diagnostic and research value of the remaining data for medical AI and drug discovery applications.
Data Source
AI summary
An information processing method according to an aspect of the present disclosure causes a computer to execute operations to thereby generate data for medical AI/drug discovery AI, the operations including: acquiring multiple image information (acquisition unit), in which each of the multiple image information is generated by a medical device having one or more imaging functions and private information contained in such image information has been subjected to an anonymization process (anonymization unit); acquiring multiple clinical information related to medical practice for a patient, in which private information associated with the multiple clinical information has been anonymized; selecting target image information suitable as data for medical AI/drug discovery AI from among the multiple image information, selecting target clinical information to be used as the data for medical AI/drug discovery AI from among the multiple clinical information (selection unit); associating disease information related to a disease and organ information related to an organ with the target image information and the target clinical information (association unit); and performing standardization on the target image information and the target clinical information (standardization unit).