AI Diagnosis Assistant for Test Plan Optimization
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Solution Overview
Problem
Current medical practices require extensive manual work from doctors to arrange test plans and treatment plans based on patient symptoms and physical findings, leading to long working hours, potential errors, and variability in diagnosis and treatment due to differences in knowledge and experience.
Innovation Solution
A diagnosis and prescription support system utilizing an additionally trained large language model that learns from a large number of diagnosed cases, proposes efficient test plans, integrates test results, and suggests appropriate treatment plans based on confirmed diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual arrangement of test plans and treatment plans by doctors is performed, then diagnostic accuracy and treatment appropriateness can be maintained through doctor's knowledge and experience, but working hours become excessively long and productivity decreases
Solution Approach 1:
An AI assistant system acts as an intermediary between doctors and patient data, automatically generating test plans and treatment plans based on patient information. The AI processes symptoms, physical findings, and medical history to propose diagnostic and treatment arrangements, freeing doctors from manual documentation while maintaining diagnostic quality through AI-generated recommendations that doctors can review and approve.
Solution Approach 2:
The system enables self-service by allowing the AI to autonomously generate test plans and treatment plans without requiring manual input from doctors for each case. The AI independently analyzes patient data, retrieves relevant medical knowledge, and produces complete care plans that doctors can efficiently review, significantly reducing the time doctors spend on administrative tasks.
2Adaptability or versatility
If manual arrangement of medical instructions is performed by doctors, then individualized care can be provided based on doctor's judgment, but errors in diagnosis and unnecessary tests occur due to differences in knowledge and experience levels
Solution Approach 1:
The AI system incorporates feedback mechanisms by continuously learning from corrected cases and outcome data. When doctors modify AI-generated plans or when diagnostic outcomes are recorded, the system uses this feedback to improve future recommendations. This ensures that individualized care maintains high diagnostic consistency across different practitioners while adapting to specific patient needs.
Solution Approach 2:
The AI system provides universal diagnostic support that works consistently across all cases and practitioners. It integrates multiple functions including symptom analysis, differential diagnosis generation, test plan creation, and treatment recommendation, ensuring that every patient receives care based on the same evidence-based guidelines regardless of which doctor is treating them.
3Reliability
If extensive tests are arranged to confirm diagnosis, then diagnostic accuracy can be improved, but loss of time increases due to multiple tests and procedures
Solution Approach 1:
The AI system applies partial action by generating prioritized test plans that focus on the most critical and high-yield tests first. Instead of arranging all possible tests simultaneously, it sequences tests based on diagnostic probability and urgency, allowing doctors to order tests in stages. This reduces initial time loss while maintaining diagnostic accuracy through progressive testing based on results.
Solution Approach 2:
The AI performs preliminary action by pre-generating comprehensive test plans and treatment plans before the doctor reviews them. It proactively analyzes patient data, identifies necessary tests, and prepares complete care arrangements in advance, eliminating the need for doctors to manually think through and arrange each test sequentially, thereby significantly reducing time loss.
Data Source
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AI summary
To support obtaining a confirmed diagnosis by causing a large language model to additionally learn patient records of a large number of diagnosed cases, causing the additionally trained large language model to propose an efficient test plan for confirming a diagnosis from symptom findings obtained from an undiagnosed patient visiting a hospital, and integrating obtained test results. An electronic health record system using a large language model in combination includes: a "possible diagnosis name, additional symptom, physical finding, and test answering means" that answers at least one of a list of possible diagnosis names and probabilities of the diagnosis names, and a recommended additional symptom, physical finding, and test; and a sequential diagnosis improvement means for repeating a "symptom, physical finding, and test result data provision means" and the "possible diagnosis name, additional symptom, physical finding, and test answering means" until the list of possible diagnosis names and probabilities of the diagnosis names has sufficient accuracy for confirmed diagnosis.