Abstract: An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

Abstract: An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

Abstract: The present invention relates to biometric identification using a combination of static pattern characteristic unique to an individual and a measure of a dynamically changing blood flow characteristic for the individual. Images are acquired of the microvasculature of the outer eye (e.g. the sclera) which presents a unique pattern for each individual which can be determined using image processing and artificial intelligence (AI) techniques. A series of images are acquired of the microvasculature of the outer eye (e.g. the sclera) at a higher magnification thereby allowing for the determination of blood flow (e.g. velocity) characteristics using computer vision and AI techniques. The pattern and blood flow characteristics are used in combination to determine the identity of the individual.

Abstract: The present invention relates to biometric identification using a combination of static pattern characteristic unique to an individual and a measure of a dynamically changing blood flow characteristic for the individual. Images are acquired of the microvasculature of the outer eye (e.g. the sclera) which presents a unique pattern for each individual which can be determined using image processing and artificial intelligence (AI) techniques. A series of images are acquired of the microvasculature of the outer eye (e.g. the sclera) at a higher magnification thereby allowing for the determination of blood flow (e.g. velocity) characteristics using computer vision and AI techniques. The pattern and blood flow characteristics are used in combination to determine the identity of the individual.

Abstract: This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

Abstract: An AI-based system and method for providing warning of potential health concerns. The invention involves receiving patient information from various sources, including one or more of sensor data, patient-reported symptoms, and medical records. The patient information is processed using AI models to identify potential health concerns. The AI models may identify user-specific patterns and/or metrics associated with potential health concerns. Tiered alerts may be generated and provided to a user based on the AI analysis. Recommended actions may be generated and provided to the user based on the alert.

Abstract: A handheld medical diagnostic device integrates at least seven sensor modules, including a high-magnification camera, otoscope camera, stethoscope, infrared thermometer, EKG, pulse oximeter, body composition monitor, glucometer, and/or hematology analyzer. The device features a system-on-chip (SoC) processor for intelligent data management, edge computing, and cloud-based AI processing, while applying privacy-preserving techniques. A wireless transceiver enables data transmission and reception between the sensors, edge compute nodes, and cloud platforms. The ergonomically designed housing includes a display with a force-sensitive layer for user input and navigation. The high-magnification camera module offers 300× magnification for visualizing blood cells and skin, with an annular LED array, encryption engine, and physically unclonable function (PUF) circuit. The otoscope camera module features a narrow profile for ear, nose, and throat imaging, with an annular LED array.

Abstract: This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

Abstract: A handheld medical diagnostic device integrates at least seven sensor modules, including a high-magnification camera, otoscope camera, stethoscope, infrared thermometer, EKG, pulse oximeter, body composition monitor, glucometer, and/or hematology analyzer. The device features a system-on-chip (SoC) processor for intelligent data management, edge computing, and cloud-based AI processing, while applying privacy-preserving techniques. A wireless transceiver enables data transmission and reception between the sensors, edge compute nodes, and cloud platforms. The ergonomically designed housing includes a display with a force-sensitive layer for user input and navigation. The high-magnification camera module offers 300× magnification for visualizing blood cells and skin, with an annular LED array, encryption engine, and physically unclonable function (PUF) circuit. The otoscope camera module features a narrow profile for ear, nose, and throat imaging, with an annular LED array.

Abstract: This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

Abstract: An AI-based system and method for providing warning of potential health concerns. The invention involves receiving patient information from various sources, including one or more of sensor data, patient-reported symptoms, and medical records. The patient information is processed using AI models to identify potential health concerns. The AI models may identify user-specific patterns and/or metrics associated with potential health concerns. Tiered alerts may be generated and provided to a user based on the AI analysis. Recommended actions may be generated and provided to the user based on the alert.

Abstract: A system for serving personalized advertisements based on biometric and identifying data captured by sensors. The system employs pre-processing, feature extraction, and recognition modules to identify individuals. A context-aware processing module adapts recognition strategies based on environmental data and analyzes personal information to confirm the identity of the targeted individual and send targeted advertisements. The system includes display and speaker modules for visual and audio advertisements, a predictive interaction modeling module for generating personalized advertisements, and a cross-platform behavior analysis module for analyzing user behavior across offline and online platforms.

Abstract: An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

Abstract: The present disclosure relates to computing blood pressure from images of the outer eye of an individual. Images of the outer eye of an individual obtained via a high magnification camera can be analyzed using computer vision to identify features associated with blood vessels in the outer eye, such as blood vessel size or diameter, blood vessel wall thickness, distance between vessels or vessel segments, area between vessels or vessel segments, and/or blood velocity through the vessels. These blood vessel features derived from images may be used to compute a blood pressure measure(s) for an individual through use of an artificial intelligence algorithm which relates the blood vessel features to blood pressure values.