AI Medical Analysis Device Next Step Guidance
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Solution Overview
Problem
Current AI analysis in medicine does not effectively determine the next steps for medical information analysis, leading to unnecessary secondary interpretations and increased burden on radiologists, as it only identifies differences between user-created and AI-processed information without providing guidance on how to proceed.
Innovation Solution
An analysis device and method that acquires and compares medically-related information through AI processing with user-created information, generating structured data to output next steps based on the comparison results, thereby guiding users on whether additional examinations or confirmations are needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI analysis is introduced to support medical diagnosis, then diagnostic efficiency is improved, but the system does not provide clear next step guidance leading to increased workload on secondary radiologists
Solution Approach 1:
The system implements feedback by comparing AI analysis results with user-created information and providing next step recommendations. The comparison processor analyzes agreement between AI and user interpretations, then the outputter provides feedback guidance on whether secondary interpretation is necessary, creating a closed-loop system that reduces unnecessary work while maintaining diagnostic quality
Solution Approach 2:
The system enables self-service by automatically determining whether secondary radiologist interpretation is needed based on the comparison between AI and user interpretations. The analysis device autonomously evaluates diagnostic agreement and generates next step recommendations without requiring manual assessment, allowing the system to self-regulate its own workflow efficiency
2Productivity
If AI analysis is introduced to support medical diagnosis, then diagnostic efficiency is improved, but the burden on physicians is not reduced due to lack of optimized workflow
Solution Approach 1:
The system reduces physician burden through feedback mechanisms that provide clear next step recommendations based on AI-user interpretation comparison. The outputter communicates whether secondary interpretation is needed, eliminating ambiguity and allowing physicians to focus their efforts only on cases requiring additional review
Solution Approach 2:
The system changes the operational parameters of diagnostic workflow by introducing automated comparison and next step determination. This transforms the workflow from a fixed multi-step process to a dynamic, condition-based process where the number of required steps varies based on AI-user agreement, thereby optimizing physician workload
3Reliability
If secondary interpretation is performed in all cases, then diagnostic accuracy is maintained, but the burden on secondary radiologists increases unnecessarily
Solution Approach 1:
The system applies partial action by performing secondary interpretation only when necessary, rather than in all cases. The comparison processor identifies cases where AI and user interpretations disagree, and the outputter recommends secondary review only for those specific cases, eliminating excessive action while maintaining diagnostic accuracy where AI and user interpretations agree
Solution Approach 2:
The system uses feedback from the comparison between AI and user interpretations to dynamically determine the need for secondary review. This feedback mechanism ensures that secondary radiologists are engaged only when diagnostic uncertainty exists, maintaining reliability while optimizing productivity
Data Source
AI summary
An analysis device includes a hardware processor, an acquirer, and an outputter, wherein the hardware processor acquires first medically-related information obtained through computer processing performed on medical information, the hardware processor generates structured data by structuring unstructured data acquired from user-created information on the basis of the medical information, the acquirer acquires second medically-related information from the structured data, the hardware processor compares the acquired first medically-related information and the second medically-related information acquired by the acquirer, and the outputter outputs next step information on the basis of a comparison result from the comparing.