18072241. SYSTEM FOR DIAGNOSIS DECISION SUPPORT BY AN AI ASSISTED AND OPTIMIZED MONITORING GUIDANCE TOOL, AND ASSOCIATED METHOD simplified abstract (GE Precision Healthcare LLC)
Contents
- 1 SYSTEM FOR DIAGNOSIS DECISION SUPPORT BY AN AI ASSISTED AND OPTIMIZED MONITORING GUIDANCE TOOL, AND ASSOCIATED METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM FOR DIAGNOSIS DECISION SUPPORT BY AN AI ASSISTED AND OPTIMIZED MONITORING GUIDANCE TOOL, AND ASSOCIATED METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEM FOR DIAGNOSIS DECISION SUPPORT BY AN AI ASSISTED AND OPTIMIZED MONITORING GUIDANCE TOOL, AND ASSOCIATED METHOD
Organization Name
Inventor(s)
Isani Mishra of Bangalore (IN)
Guy Robert Vesto of Kildeer IL (US)
Brian Dale Janssen of Brookfield WI (US)
SYSTEM FOR DIAGNOSIS DECISION SUPPORT BY AN AI ASSISTED AND OPTIMIZED MONITORING GUIDANCE TOOL, AND ASSOCIATED METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18072241 titled 'SYSTEM FOR DIAGNOSIS DECISION SUPPORT BY AN AI ASSISTED AND OPTIMIZED MONITORING GUIDANCE TOOL, AND ASSOCIATED METHOD
Simplified Explanation
The system described in the patent application is designed to diagnose and monitor patients using an edge device, patient monitoring devices, and a database of medical information. The edge device uses an artificial intelligence model to generate a list of potential diagnoses for a patient based on their medical information, along with predicted probabilities for each diagnosis. The system then provides this information to the user and collects their predictions for the diagnoses.
- Edge device, patient monitoring devices, and database of medical information used for diagnosing and monitoring patients.
- Artificial intelligence model generates a list of potential diagnoses and predicted probabilities based on patient's medical information.
- User provides predictions for the diagnoses.
Potential Applications
This technology could be applied in various medical settings such as hospitals, clinics, and telemedicine platforms to assist healthcare providers in diagnosing and monitoring patients more efficiently and accurately.
Problems Solved
This technology helps in improving the accuracy and speed of diagnosing patients, leading to better treatment outcomes and potentially reducing healthcare costs by avoiding unnecessary tests or treatments.
Benefits
- Enhanced diagnostic accuracy - Efficient patient monitoring - Potential cost savings in healthcare
Potential Commercial Applications
- Healthcare institutions - Telemedicine companies - Medical device manufacturers
Possible Prior Art
One possible prior art could be existing systems that use artificial intelligence for medical diagnosis and monitoring, but the specific combination of edge devices, patient monitoring devices, and user input for predictions may be unique to this patent application.
Unanswered Questions
How does the system ensure the privacy and security of patient data?
The patent application does not provide details on the measures taken to protect patient information from unauthorized access or breaches.
What is the accuracy of the AI model in generating differential diagnoses?
The patent application does not mention any specific data or studies on the accuracy of the AI model in predicting diagnoses for patients.
Original Abstract Submitted
A system for diagnosing and monitoring one or more patients. The system including an edge device, a plurality of patient monitoring devices, and a database including medical information. The edge device being configured to obtain medical information corresponding to a patient; generate, by an artificial intelligence (AI) model, a differential diagnosis list for the patient based on the medical information, the differential diagnosis list including one or more diagnoses and a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis; provide the differential diagnosis list and predicted probabilities to the user; and obtain user predictions for one or more of the diagnoses.
