Real-Time AI-Powered Image Processing Framework for Remote Healthcare Diagnostics over 5G

Abstract

This invention presents a Real-Time AI-Powered Image Processing Framework for Remote Healthcare Diagnostics over 5G. The Distributed AI-Based Imaging Assistant (DABIA) is a real-time diagnostic system that leverages AI-powered image processing, federated learning, and 5G connectivity for remote healthcare applications. It introduces an adaptive diagnostic feedback loop, allowing healthcare providers to receive real-time, patient-specific diagnostic insights based on individual profiles and environmental factors. DABIA's edge-to-cloud synchronization reduces latency, enabling time-sensitive diagnostics in mobile and rural healthcare environments. Privacy-preserving federated learning ensures data security, making DABIA a scalable, regulatory-compliant solution for telemedicine and remote diagnostics. Accompanied Drawing [FIG. 1]

Specification

Description:[001] The present invention relates to remote healthcare diagnostics and real-time imaging systems. Specifically, it introduces a novel framework, Distributed AI-Based Imaging Assistant (DABIA), that leverages artificial intelligence (AI), edge computing, and 5G technology to provide adaptive, low-latency diagnostics and healthcare monitoring in real-time. The invention finds applications in telemedicine, remote surgery, chronic disease monitoring, and mobile healthcare environments.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Remote healthcare has gained significant importance, particularly with the rise of telemedicine. Challenges such as diagnostic latency, data privacy concerns, and the need for high accuracy in patient monitoring have impeded the widespread adoption of remote diagnostics. Current systems lack the adaptive mechanisms to cater to a diverse range of environmental factors, patient demographics, and resource availability in real-time, which is essential for effective remote diagnostics. Further, privacy risks and regulatory constraints limit data sharing across various jurisdictions, affecting diagnostic consistency.
[004] Traditional image processing frameworks are limited by network speed, leading to delays in healthcare service delivery. Moreover, existing systems often require centralized processing, which increases latency and reduces the system's ability to adapt to different resource levels available in diverse settings. Therefore, there is a need for an innovative diagnostic solution that can dynamically learn and process data, providing insights to practitioners promptly while respecting data privacy.
[005] Accordingly, to overcome the prior art limitations based on aforesaid facts. The present invention provides a Real-Time AI-Powered Image Processing Framework for Remote Healthcare Diagnostics over 5G. Therefore, it would be useful and desirable to have a system, method and apparatus to meet the above-mentioned needs.

SUMMARY OF THE PRESENT INVENTION
[006] The Distributed AI-Based Imaging Assistant (DABIA) is designed to overcome the limitations of current remote diagnostic frameworks. It integrates AI-based image processing algorithms, edge and cloud computing, and adaptive learning mechanisms to offer a real-time, personalized diagnostic system. DABIA utilizes 5G for low-latency connectivity, enabling high-speed image and data transfer between healthcare facilities, edge devices, and cloud servers.
[007] DABIA introduces a feedback loop that dynamically refines diagnostics based on patient demographics, environmental conditions, and healthcare history. This feedback loop, together with federated learning, ensures that the system can adapt and improve without requiring centralized data storage. The invention addresses challenges in latency, data privacy, and adaptability, offering a scalable solution for remote healthcare diagnostics that is well-suited for mobile healthcare units, emergency medical services, and rural healthcare delivery.
[008] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[009] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[010] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[011] Figure 1: Block diagram of the DABIA framework, illustrating the integration of AI-powered imaging, edge computing, and cloud processing.
[012] Figure 2: Flowchart depicting the Adaptive Diagnostic Feedback Loop, detailing how real-time feedback refines diagnostic results.
[013] Figure 3: System architecture showcasing the interaction between edge devices, cloud servers, and the federated learning model.
[014] Figure 4: Functional diagram for predictive diagnosis, highlighting the role of patient history and environmental data in generating patient-specific models.
[015] Figure 5: An example application setup in a mobile healthcare environment, where the DABIA system performs on-site diagnostics.
[016] Figure 6: Data privacy and encryption model for secure handling of patient data.
DETAILED DESCRIPTION OF THE INVENTION
[017] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[018] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[019] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
This invention presents an advanced image and video compression system that combines hybrid neural networks (Variational Autoencoders, Generative Adversarial Networks, and Transformers) with quantum computing and edge computing for enhanced efficiency and scalability. The system compresses data by encoding it into a latent space, reconstructing high-quality images, and capturing dependencies across video frames. Quantum processors handle intensive computations, while edge computing facilitates real-time compression closer to data sources. Auxiliary data and meta-learning optimize compression for varying content, and a reinforcement learning agent ensures adaptive data flow in fluctuating network conditions. This system is suited for applications requiring high-quality, low-latency compression, such as streaming, telemedicine, and AR/VR.
System Overview
[020] System Overview
The Distributed AI-Based Imaging Assistant (DABIA) is a multi-layered system designed to facilitate remote diagnostics through distributed AI processing and real-time feedback mechanisms. The primary components of the system include edge devices equipped with AI-powered imaging software, 5G network connectivity, and a federated learning-based cloud model.
[021] Edge Device and Imaging Module
Edge devices play a crucial role in pre-processing medical images captured from diagnostic instruments such as portable ultrasound devices, X-ray machines, and wearable health monitors. These devices contain AI models capable of conducting preliminary diagnostic analyses directly on the device. The imaging module further refines the captured images by filtering noise and adjusting image clarity based on environmental lighting conditions, thereby providing high-quality images for remote analysis.
[022] Adaptive Diagnostic Feedback Loop
The feedback loop allows real-time adaptation of diagnostic outputs based on the incoming data stream. When a new diagnostic image is processed, the feedback loop cross-references the findings with the patient's health profile and historical data, enhancing diagnostic accuracy. The loop also adapts to specific environmental conditions, such as light interference and motion, making it effective in mobile or rural healthcare environments.
[023] Federated Learning for Privacy-Preserving Model Updates
DABIA employs federated learning to train and update AI models without requiring raw patient data to leave the local environment. Each edge device or local server updates the model based on new cases it encounters, contributing anonymized insights to a central cloud-based model. This model receives aggregated learning data, reducing privacy risks and maintaining compliance with health data regulations across different regions.
[024] Predictive Diagnosis Using Patient-Specific Models
DABIA incorporates predictive diagnostic capabilities by analyzing patient history, demographic data, and geographic information. For example, the system may consider genetic predispositions or environmental risk factors specific to a region, such as high rates of infectious disease or pollution. The patient-specific model is built using adaptive algorithms that refine predictions based on new inputs, ensuring personalized and highly relevant diagnostic insights.
[025] Edge-to-Cloud Synchronization
Edge devices handle preliminary, time-sensitive processing, while more complex computations are performed in the cloud. This division of tasks reduces latency, as urgent analyses (e.g., detecting stroke symptoms) are performed at the edge. Less urgent data, such as full-body scans, is sent to the cloud for deeper analysis. The synchronization between the edge and cloud components is managed by a 5G network, which ensures minimal delay.

[026] Data Security and Privacy Measures
DABIA integrates advanced encryption protocols and privacy-preserving AI techniques to ensure data security. Patient data is encrypted during transmission and anonymized for model training purposes. By employing secure data handling processes and federated learning, the system complies with regulatory standards, including GDPR and HIPAA.
[027] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[028] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[029] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. A distributed diagnostic system (DABIA) for remote healthcare that utilizes AI-driven image processing at edge devices connected to a cloud model via a 5G network, characterized by a real-time Adaptive Diagnostic Feedback Loop that adjusts diagnostics based on patient data and environmental factors.
2. The diagnostic system of claim 1, wherein the edge device performs preliminary diagnostic image processing, reducing latency in urgent cases through real-time AI algorithms.
3. The diagnostic system of claim 1, wherein federated learning is employed to update central AI models based on insights derived from local patient data without transmitting raw data, enhancing data privacy.
4. The diagnostic system of claim 1, wherein predictive diagnostic capabilities are generated using a patient-specific model that integrates historical patient data, geographic factors, and demographic information.
5. The diagnostic system of claim 1, wherein edge-to-cloud synchronization dynamically allocates processing tasks between edge and cloud, optimizing latency and bandwidth utilization according to network conditions.
6. The diagnostic system of claim 1, further comprising encryption and data anonymization protocols to ensure compliance with healthcare data privacy regulations.

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