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US-20260128138-A1 - AI-Based System and Method for Generating Enhanced Radiology Reports

US20260128138A1US 20260128138 A1US20260128138 A1US 20260128138A1US-20260128138-A1

Abstract

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

Inventors

  • Alexander Davis

Assignees

  • Alexander Davis

Dates

Publication Date
20260507
Application Date
20241103

Claims (20)

  1. 1 . A system for generating an enhanced radiology report, said system comprising: a database configured to store patient data, wherein said patient data includes one or more of radiology images, blood test results, physical examination records and patient-reported symptoms; a natural language processing (NLP) module configured to extract relevant clinical information from the patient data stored in said database; a machine learning module comprising an application-specific integrated circuit (ASIC) for an artificial neural network connected to the database, the ASIC comprising: a plurality of neurons organized in an array, wherein each neuron comprises a register, a processing element and at least one input, and a plurality of synaptic circuits, each synaptic circuit including a memory for storing a synaptic weight, wherein each neuron is connected to at least one other neuron via one of the plurality of synaptic circuits trained to correlate said extracted clinical information with said radiology images to identify relationships and generate diagnostic insights; an artificial intelligence (AI) based radiology report generation module configured to: i. analyze a radiology image in conjunction with said correlated clinical information from said machine learning module; ii. generate a preliminary radiology report based on said analysis, wherein said preliminary report optionally includes an AI-generated diagnosis and visual highlights of regions of interest on said radiology image; iii. receive radiologist input modifying or confirming said preliminary radiology report; and iv. update said machine learning module based on said radiologist input; and v. a report integration module configured to integrate said AI-generated radiology report into a patient's electronic health record.
  2. 2 . The system of claim 1 , wherein said AI-based radiology report generation module employs a multimodal deep learning architecture that integrates natural language processing of clinical notes, computer vision analysis of radiology images, and structured data from lab results and vital signs to generate a holistic diagnostic assessment.
  3. 3 . The system of claim 1 , wherein said machine learning module incorporates an active learning framework that selectively prompts radiologists for input on informative and uncertain cases, whereby optimizing efficiency of capturing expert feedback for continuous improvement of the AI system.
  4. 4 . The system of claim 1 , further comprising: an explainable AI module that generates human-interpretable visual and textual explanations of key factors influencing the AI-generated diagnostic predictions, thereby enhancing transparency and building trust with radiologists and patients.
  5. 5 . The system of claim 1 , wherein said report integration module applies natural language generation techniques to automatically summarize key findings and recommendations from the AI-generated radiology report into a concise format for inclusion in the patient's EHR.
  6. 6 . The system of claim 1 , further comprising: a clinical decision support module that integrates the AI-generated radiology insights with evidence-based guidelines, relevant clinical trials, and similar past cases, thereby providing radiologists with contextually relevant diagnostic and treatment recommendations.
  7. 7 . The system of claim 1 , wherein said machine learning module incorporates a reinforcement learning framework that automatically adapts hyperparameters and architectures of underlying deep learning models based on a reward signal derived from radiologist feedback and patient outcomes.
  8. 8 . The system of claim 1 , further comprising: a federated learning module that enables the AI system to securely learn from decentralized patient data across multiple institutions without requiring data sharing, thereby enhancing generalizability and robustness of the diagnostic models.
  9. 9 . The system of claim 1 , wherein said NLP module employs a question-answering architecture that can automatically extract clinically relevant information from the patient's EHR to answer radiologists' queries and provide contextual insights during a diagnostic process.
  10. 10 . The system of claim 1 , further comprising: a predictive analytics module that leverages the AI-generated radiology insights, along with longitudinal EHR data, to predict patient trajectories, identify high-risk individuals, and recommend proactive interventions for improving outcomes and reducing costs.
  11. 11 . A method for generating an expanded radiology report, said method comprising: accessing, from a database, a radiology image and associated patient data, wherein said associated patient data includes at least one selected from the group consisting of blood test results, physical examination records, and patient-reported symptoms; extracting, by a natural language processing (NLP) module, relevant clinical information from the accessed patient data; correlating, by a machine learning module, said extracted clinical information with said radiology image to identify relationships and generate diagnostic insights; analyzing, by an artificial intelligence (AI) based radiology report generation module, said radiology image in conjunction with said correlated clinical information; generating a preliminary radiology report based on said AI analysis, wherein said preliminary report optionally includes a suggested diagnosis and visual highlights of regions of interest on said radiology image; receiving radiologist input modifying or confirming said preliminary radiology report; updating said machine learning module based on said received radiologist input; and integrating said AI-generated radiology report into the patient's electronic health record.
  12. 12 . The method of claim 11 , wherein said analyzing by the AI-based radiology report generation module employs a multimodal deep learning architecture that integrates natural language processing of clinical notes, computer vision analysis of radiology images, and structured data from lab results and vital signs to generate a holistic diagnostic assessment.
  13. 13 . The method of claim 11 , wherein said correlating by the machine learning module incorporates an active learning framework that selectively prompts radiologists for input on informative and uncertain cases, whereby optimizing efficiency of capturing expert feedback for continuous improvement of the AI system.
  14. 14 . The method of claim 11 , further comprising: generating, by an explainable AI module, human-interpretable visual and textual explanations of key factors influencing the AI-generated diagnostic predictions, thereby enhancing transparency and building trust with radiologists and patients.
  15. 15 . The method of claim 11 , wherein said integrating the AI-generated radiology report into the patient's electronic health record comprises: applying natural language generation techniques to automatically summarize key findings and recommendations from the AI-generated radiology report into a concise format for inclusion in the patient's electronic health record.
  16. 16 . The method of claim 11 , further comprising: providing, by a clinical decision support module, radiologists with contextually relevant diagnostic and treatment recommendations by integrating the AI-generated radiology insights with evidence-based guidelines, relevant clinical trials, and similar past cases.
  17. 17 . The method of claim 11 , wherein said updating the machine learning module incorporates a reinforcement learning framework that automatically adapts hyperparameters and architectures of underlying deep learning models based on a reward signal derived from radiologist feedback and patient outcomes.
  18. 18 . The method of claim 11 , further comprising: securely learning, by a federated learning module, from decentralized patient data across multiple institutions without requiring data sharing, thereby enhancing generalizability and robustness of the diagnostic models.
  19. 19 . The method of claim 11 , further comprising: predicting patient trajectories, identifying high-risk individuals, and recommending proactive interventions for improving outcomes and reducing costs by a predictive analytics module that leverages the AI-generated radiology insights along with longitudinal electronic health record data.
  20. 20 . The method of claim 11 , wherein said extracting relevant clinical information by the NLP module employs a question-answering architecture that can automatically extract clinically relevant information from the patient's electronic health record to answer radiologists' queries and provide contextual insights during a diagnostic process.

Description

BACKGROUND OF THE INVENTION Field of Invention The various aspects discussed herein relate to systems and methods for generating enhanced radiology reports using artificial intelligence. Description of Related Art Radiologists analyze medical images to diagnose various health conditions. However, conventional radiology reporting workflows face several challenges. First, radiologists often lack access to a patient's complete clinical history, which can provide valuable context for interpreting images. Second, manually analyzing complex images is time-consuming and prone to human variability and errors. Third, radiology reports are often unstructured and may lack key information needed by referring physicians for optimal treatment planning. Accordingly, there is a need in the art for an AI-based radiology reporting system that integrates multimodal patient data, generates comprehensive diagnostic insights, and produces structured reports that facilitate clinical decision-making. Such a system would improve the accuracy, efficiency, and value of radiology services in patient care. BRIEF SUMMARY OF THE INVENTION This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention. The present invention provides systems and methods for generating enhanced radiology reports using artificial intelligence (AI). In one aspect, the system comprises a database for storing multimodal patient data, including radiology images, blood test results, physical exam records, and patient-reported symptoms. A natural language processing (NLP) module extracts relevant clinical information from the patient data, which a machine learning module then correlates with the radiology images to identify diagnostic insights. Another important embodiment incorporates biopsy results. an application-specific integrated circuit (ASIC) for an artificial neural network connected to the computer memory device, the ASIC comprising: a plurality of neurons organized in an array, wherein each neuron comprises a register, a processing element and at least one input, and a plurality of synaptic circuits, each synaptic circuit including a memory for storing a synaptic weight, wherein each neuron is connected to at least one other neuron via one of the plurality of synaptic circuits configured, configured to An AI-based report generation module analyzes the radiology images in conjunction with the correlated clinical information to generate a preliminary report. This report includes an AI-suggested diagnosis and visual highlights of key regions of interest on the images. The preliminary report is presented to a radiologist for review and modification. The radiologist's input is used to update the machine learning module, enabling continuous refinement of the AI system. Finally, a report integration module incorporates the AI-generated radiology report into the patient's electronic health record (EHR). The AI system may employ a multimodal deep learning architecture that integrates natural language processing of clinical notes, computer vision analysis of radiology images, and structured data from labs and vital signs. An active learning framework selectively prompts radiologists for input on uncertain cases to efficiently capture expert feedback. Explainable AI techniques provide human-interpretable visual and textual explanations of the factors influencing the AI's diagnostic predictions, enhancing transparency and trust. Additionally the present invention may include a clinical decision support module that provides evidence-based diagnostic and treatment recommendations, a reinforcement learning framework that automatically adapts the AI models based on radiologist feedback and patient outcomes, and a federated learning module enabling secure multi-institutional collaboration without data sharing. A question-answering system can automatically extract relevant information from the EHR to provide radiologists with contextual insights. Predictive analytics may leverage the AI-generated insights and longitudinal EHR data to identify high-risk patients and recommend proactive interventions. The present invention solves the problems of incomplete clinical context, time-consuming manual image analysis, and unstructured reporting associated with conventional radiology workflows. By integrating multimodal data, generating comprehensive diagnostic insights, and producing structured reports, the AI system improves the accuracy, efficiency, and clinical utility of radiology services. This enhanced radiology reporting system has the potential to streamline diagnostic processes, increase productivity, reduce errors and variability, and ultimately lead to better patient outcomes and reduced healthcare costs across a wid