Explainable AI for Mental Disease Diagnosis via Brain Wave Analysis
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Solution Overview
Problem
Current artificial intelligence systems for diagnosing mental diseases lack explainability, leading to potential errors and mistrust in medical settings, and existing methods for diagnosing and treating mental diseases are often unreliable and time-consuming, with medication side effects being a significant concern.
Innovation Solution
An explainable artificial intelligence system that uses a machine learning model to analyze brain wave signals, preprocess them through noise cancellation and epoching, and generate a decision-making structure to diagnose mental diseases, providing visualized results and stimulation information for treating specific brain regions, utilizing advanced variational autoencoders and recurrent neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional self-response questionnaires are used for diagnosis, then the diagnosis process is simple and quick, but the diagnostic accuracy and reliability are low due to subjective response bias and varying evaluation criteria
Solution Approach 1:
The patent replaces the mechanical/manual questionnaire-based diagnostic system with an automated AI system that processes brain wave signals. The machine learning model automatically analyzes EEG data to diagnose mental diseases, eliminating subjective human evaluation and response bias while providing objective, quantifiable diagnostic results based on physiological brain activity patterns
Solution Approach 2:
The patent introduces brain wave signals as an intermediary medium between the patient and the diagnostic process. Instead of directly questioning the patient (questionnaire), the system measures and analyzes objective brain electrical activity patterns that reflect mental states, providing a more reliable intermediate indicator of psychological conditions
2Productivity
If medication is used to treat mental diseases, then treatment can be provided, but it takes considerable time (1-2 years) to cure and causes many side effects
Solution Approach 1:
The patent replaces chemical medication treatment with electroceutical treatment using electrical stimulation. The system delivers controlled electrical signals to specific brain regions identified through brain wave analysis, providing a non-chemical alternative that avoids pharmacological side effects while targeting the underlying neural mechanisms of mental diseases
Solution Approach 2:
The system enables self-service treatment through portable brain wave measurement devices that patients can use at home. The AI system processes the recorded brain waves and provides treatment recommendations without requiring continuous hospital visits, making treatment more accessible and convenient while reducing the burden of long-term medication management
3Extent of automation
If a black box AI system is used for diagnosis, then automated decision-making is provided, but the system cannot explain the reasoning process leading to diagnosis, reducing trust and usability in medical settings
Solution Approach 1:
The patent implements feedback mechanisms that provide clinicians with interpretable information about the AI's diagnostic reasoning. The system outputs not only the diagnosis result but also explanations based on the analyzed brain wave features and decision-making processes, allowing medical professionals to understand, verify, and trust the automated diagnostic recommendations
Solution Approach 2:
The patent introduces a decision-making structure as an intermediary that bridges the black box AI model and the clinician. This structure visualizes the relationship between input brain wave features and diagnostic outcomes, making the internal reasoning process transparent and interpretable without requiring changes to the underlying automated machine learning model
Data Source
AI summary
An explainable artificial intelligence system includes a processor that visualizes and provides a diagnosed result, a decision-making structure which is description information for describing a basis for the diagnosis, a description of at least one second brain wave feature, and an importance of the at least one second brain wave feature.