SOAP, Inc. (20240355471). MEDICAL DIAGNOSIS GENERATION simplified abstract
Contents
MEDICAL DIAGNOSIS GENERATION
Organization Name
Inventor(s)
Steven Charlap of Boca Raton FL (US)
MEDICAL DIAGNOSIS GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240355471 titled 'MEDICAL DIAGNOSIS GENERATION
- Simplified Explanation:**
The patent application discusses the use of generative artificial intelligence to provide diagnoses for patients by applying elimination rules based on patient intake data to narrow down potential diagnoses.
- Key Features and Innovation:**
- Leveraging generative artificial intelligence for patient diagnoses
- Applying elimination rules based on patient intake data
- Narrowing down potential diagnoses from a large corpus of known medical conditions
- Classifying patient intake data into risk and symptom data
- Using machine learning models to process and reconcile patient data for improved diagnostic accuracy
- Potential Applications:**
This technology could be used in healthcare settings to assist healthcare professionals in diagnosing patients more efficiently and accurately.
- Problems Solved:**
This technology addresses the challenge of sifting through a large number of potential diagnoses to arrive at a meaningful and accurate differential diagnosis for patients.
- Benefits:**
- Improved diagnostic accuracy
- Faster diagnosis process
- Enhanced efficiency for healthcare professionals
- Commercial Applications:**
The technology could be commercialized as a diagnostic tool for healthcare providers, potentially leading to improved patient outcomes and reduced healthcare costs.
- Questions about AI:**
1. How does generative artificial intelligence improve the diagnostic process in healthcare? 2. What are the potential limitations of using AI for patient diagnoses?
Original Abstract Submitted
leveraging generative artificial intelligence to provide diagnoses for patients. in some examples, elimination rules based on patient intake data are applied to a very large corpus of candidate diagnoses, such as all known human medical diagnoses, to narrow down the very large corpus of candidates to a meaningful differential diagnosis or final diagnosis. in some examples, targeted information is elicited to determine and apply additional elimination rules to further narrow down the candidate diagnoses. in some examples, patient intake data is classified into risk data and symptom data and processed by data type-specific machine learning models. in some examples, patient intake data is reconciled and correlated between risk and symptom data by specific machine-learning models to improve diagnostic clustering and accuracy.
