Patient-Specific Medical Reports Using AI Language Tailoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical reports containing complex diagnostic information are challenging for patients to understand due to medical jargon and lack of personalization, leading to potential miscommunication and stress for both doctors and patients, especially with increasing diagnostic complexity.
Innovation Solution
A Generative AI-based system uses pre-trained language models tailored to a patient's language, education, and expectation level to generate personalized medical reports, incorporating adapted terminology and images, with AI-inpainting for easy comparison of healthy and severe states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical reports use professional medical terminology and detailed diagnostic information, then the accuracy and completeness of medical information is improved, but the comprehensibility for patients deteriorates
Solution Approach 1:
The system applies different language styles and levels of detail to different sections of the report. Professional medical terminology is used in the detailed report section for accuracy, while simplified explanations with analogies are used in the summary section for patient comprehension. This local differentiation of quality allows the same report to serve both medical professionals and patients effectively.
Solution Approach 2:
The patent introduces an AI language model as an intermediary that translates complex medical terminology into patient-friendly language. The model acts as a bridge between the detailed medical information and the patient's understanding level, converting professional terms into everyday language while preserving the essential meaning and medical accuracy.
2Adaptability or versatility
If medical reports are manually created and personalized by doctors, then the adaptability to patient-specific needs is improved, but the time consumption and workload deteriorates
Solution Approach 1:
The system enables automatic generation of personalized medical reports by having the AI language model self-configure based on patient metadata. The model automatically adjusts its language style, level of detail, and explanation approach according to the patient's education level, language preference, and comprehension needs without requiring manual intervention from doctors, thus achieving personalization at scale.
Solution Approach 2:
The patent performs preliminary configuration of the language model by training it on diverse medical communication styles and patient profiles before actual report generation. Patient metadata such as education level, language preference, and comprehension needs are pre-processed and stored, allowing the system to quickly retrieve and apply the appropriate communication style during report generation without time-consuming manual customization.
3Loss of information
If diagnostic information includes complex images and detailed findings, then the completeness of diagnostic data is improved, but the ease of patient interpretation deteriorates
Solution Approach 1:
The system extracts and separates the essential interpretive information from complex medical images and isolates the key findings that patients need to understand. Rather than presenting raw complex images, the system extracts the most relevant visual findings and presents them with simplified explanations, removing the overwhelming complexity while preserving the essential diagnostic information.
Solution Approach 2:
Instead of presenting complex images and requiring patients to interpret them, the patent inverts the approach by presenting simplified visual representations or annotated images that highlight only the relevant findings, accompanied by plain language explanations. The complexity is reversed from the traditional approach, making visual information as accessible as textual information.
4Adaptability or versatility
If doctors manually communicate diagnostic information to patients, then the ability to address patient concerns is improved, but the stress and potential for miscommunication deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the AI language model generates multiple versions of explanations and can adapt based on patient responses. The model can detect confusion or misunderstanding through patient feedback and re-explain concepts in different ways, continuously improving the communication based on the interaction rather than being a static one-way transmission of information.
Data Source
AI summary
A computer-implemented system for automatically generating patient-specific medical reports includes a data collection module configured to obtain patient-specific information, including patient-specific diagnostic information and communication-related patient-specific meta information. The system further includes an artificial intelligence (AI) module configured to process the obtained patient-specific information to generate a patient-specific report in a format tailored to the patient's language, education, and expectation level, wherein the AI module includes a pre-trained language model trained using medical terms, texts, reports, and related images for different age, language, and educational level groups. The system further includes an output module configured to output the generated report.