27 results about "Medical profession" patented technology

The invention discloses a teaching corpus construction method and system based on deep learning, and a medium, and belongs to the cross technical field of medical education, artificial intelligence and multi-modal data processing. The method comprises the following steps: a data acquisition and annotation stage: acquiring medical inquiry video data of different scenes; in the model training stage, a multi-modal deep learning model architecture is used for carrying out cross-modal alignment on multi-modal data; then semantic understanding and question classification are carried out based on the pre-training language model after fine tuning; in the corpus construction and optimization stage, multi-modal data are integrated, a corpus management system is built, dynamic updating and self-adaptive optimization are carried out on a corpus, meanwhile, a typical non-language posture library is constructed for a regional culture background, and teaching is carried out in combination with the corpus. According to the invention, in combination with medical professional knowledge, an efficient and intelligent medical inquiry video teaching corpus is constructed through deep fusion of natural language processing, computer vision, speech recognition and multi-modal data fusion technologies.

The invention relates to a medication recommendation method and system. The method comprises the following steps: preprocessing original medical data of a current patient into a standardized time sequence diagnosis and treatment sequence; inputting the time sequence diagnosis and treatment sequence and the medical professional knowledge base into a code-doctor-seeing double-level attention model for processing to obtain code-level aggregation features of each doctor seeing and corresponding doctor-seeing-level attention weight, and performing weighted aggregation on the code-level aggregation features based on the doctor-seeing-level attention weight to obtain a code-level aggregation feature; generating a global health representation of the current patient; based on at least one of the multiple medical feature information of the current patient, similar historical cases are retrieved from a case information base to serve as reference cases; and generating and outputting a medication recommendation list for the current patient at least based on the global health representation of the current patient and the medication scheme of the reference case. By adopting the method and the device, the accuracy and the safety of drug recommendation for the current patient can be improved, so that a doctor can be better assisted to make a drug decision.

Apparatuses, systems, and methods are disclosed to manage and process surgical data, and enable collaboration between two or more surgeons or other clinicians. A repository may be created that includes surgical data (including video data and / or 2024 / 030683 or radiological image data) that may be selectively shared between medical professionals. One or more trained neural networks may process the annotated surgical reports in order to populate the repository. Additional trained neural networks may generate surgical recommendations in response to user requests, based on contents of the repository. Other trained neural networks may generate surgical templates to guide a surgeon during an operation.

In some aspects, a triage artificial intelligence receives a portion of a conversation between a doctor and a patient, determines, based on the conversation, one or more symptoms associated with the patient, determines that the conversation includes a set of trigger words associated with a particular symptom of the one or more symptoms, selects based on the set of trigger words associated with the particular symptom, a first artificial intelligence specialist in a first medical specialty from a plurality of artificial intelligence specialists, and provides the first artificial intelligence specialist with access to the conversation between the doctor and the patient. The first artificial intelligence specialist provides one or more decision support insights that are sent to a computing device associated with the doctor.

Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.

According to the PDF document structuring processing and conversion tool, contents (including texts, tables, images, structures and the like) in PDF documents are analyzed and converted into structured data (Markdown) capable of being used for downstream LLM tasks, and the PDF structuring processing and conversion tool is particularly suitable for PDFs of complex typesetting of academic literatures, reports and the like. Through a literature vectorization function, medical professional literatures can be precisely processed, core ideas, key data and logic relations in the literatures can be deeply understood, vectors capable of accurately reflecting the essence of the literatures can be generated, important information is prevented from being lost or misread, and information processing and application effects are improved. Meanwhile, the personalized Agent can perform adaptive optimization aiming at diversified tasks in the medical academic field, and can fit research directions, knowledge backgrounds and working habits of different users, for example, the personalized Agent provides support conforming to a rigorous logic structure and standard academic expression during academic paper writing; and when the academic slide is manufactured, concise and clear contents with highlighted key points can be presented, so that the working efficiency of medical workers and researchers is effectively improved, and the high-standard requirements of the medical field are met.

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

The present application relates to a structured information processing method for imaging report text, a lung disease monitoring method and system. The structured information processing method includes: S11, further dividing the part and morphological features in the imaging medical professional entity into two categories, negative and positive, to obtain eight entity labels, namely, vacancy filler, sentence starter, sentence terminator, part-negative, part-positive, morphology-negative, morphology-positive and disease name, performing named entity recognition on the imaging report text based on the eight entity labels, and extracting the named entity output in BIO format; S12, filtering out redundant information marked as O and entity labels as part-negative and morphology-negative based on the extracted named entities, calculating the sentence vector and storing it in the database. The present application realizes the efficient extraction of symptom information and obtains a more expressive sentence vector, so that a more accurate cluster of similar cases can be obtained for spatiotemporal distribution feature analysis.

A method of providing guidance to medical professionals with a system including electronic devices connected on a network, one or more processors, and a database storing an authoritative knowledge base. The method comprises receiving, at a first electronic device, a prompt from a first medical professional, and converting the prompt into a machine-searchable string. A trained machine learning model is directed to retrieve one or more results from the authoritative knowledge base. A query may be transmitted to a second medical professional if the trained machine learning model determines that the one or more results are not sufficiently relevant. A reply to the query from the second medical professional may be received, and the reply may be outputted on the first electronic device.

The invention discloses an intelligent diagnosis system and method for multiple myeloma based on multi-modal data fusion, and relates to the technical field of myeloma. Comprising a medical professional module, a digital cloned construction module, a continuous optimization module, an interactive service module, a service requirement module and a basic service module, wherein the medical professional module is responsible for providing professional knowledge support related to multiple myeloma, including clinical manifestations, laboratory examination, diagnostic standards, prognosis layering and therapeutic schedules. According to the intelligent diagnosis system and method for multiple myeloma based on multi-modal data fusion, multi-channel medical knowledge can be acquired and integrated in real time, standardized and structured professional knowledge support is provided through systematic knowledge graph construction and algorithm reasoning, meanwhile, multi-source heterogeneous medical data are comprehensively integrated, and the diagnosis efficiency is improved. And constructing a full-life-cycle data file of the patient so as to provide personalized diagnosis standards and treatment schemes in combination with personal information, medical history and multi-dimensional examination results of the patient.

The invention provides an intelligent question and answer method and device suitable for lung health and electronic equipment, and is applied to a question and answer scene of pulmonary nodule or lung cancer, the method comprises the following steps: analyzing a received user question, and establishing bidirectional mapping between the user question and medical terms; retrieving dynamic knowledge based on bidirectional mapping, and generating a dynamic knowledge graph; generating a personalized decision tree corresponding to the user question according to the dynamic knowledge graph; and generating medical expressions based on the personalized decision tree, converting the medical expressions into text expressions convenient for the user to understand, and outputting the text expressions. According to the method, the latest medical professional knowledge is integrated in real time through the dynamic knowledge graph, and the professional medical expression is converted into the universal text expression to be output, so that the user experience is improved.

The present invention discloses a medical big language question-answering method based on data desensitization, which belongs to the field of data processing technology and includes: obtaining training data from a medical field corpus and preprocessing it, and obtaining processed training data through a data desensitization model; establishing a medical big language question-answering model based on an improved enhanced Transformer sub-model; inputting the processed training data into the medical big language question-answering model for training to obtain a trained medical big language question-answering model; and performing medical question-answering using the trained medical big language question-answering model. The present invention adopts an enhanced Transformer module to enhance the reasoning ability of long-distance contexts. Through preprocessing, the model's generalization ability of medical expertise is improved, and it can generate more accurate and professional suggestions; through the data desensitization model, sensitive information in medical data is identified and converted, thereby improving the accuracy and efficiency of privacy protection.

The invention provides a medical question and answer risk control method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the legality detection of the medical question content inputted by a user; when the legality detection result is passed, generating current answer content corresponding to the medical question content by using a question and answer large model, and performing streaming output of the current answer content; according to the continuously iteratively updated medical risk control knowledge base, performing medical risk control detection on the current answer content in real time by using a preset auditing model to obtain a current risk control detection result; and filtering and outputting the current answer content according to the current risk control detection result. According to the method, wrong medical information output caused by AI model defects can be effectively reduced, and the accuracy, safety and reliability of medical consultation and suggestions are ensured, so that the content safety risk problem of AI medical products is solved, and it is ensured that medical contents generated by AI conform to laws and regulations and medical professional standards.

The present invention discloses a medical machine translation method based on reinforcement learning, comprising the steps of constructing a parallel corpus, establishing a machine translation model, pre-training the basic parameters of the machine translation model, constructing a test set to calculate the translation quality score, constructing a reinforcement learning training set, and fine-tuning the optimized parameters of the machine translation model. This invention makes the translation of professional content in the medical vertical field more accurate and convenient, helping medical professionals, research institutes, institutions, and pharmaceutical and medical device companies to quickly understand and learn about the world's most advanced medical information knowledge and medical industry development trends, effectively improving the clinical diagnosis and treatment level and consultation efficiency of medical practitioners.

Abstract SYSTEM AND METHOD FOR PATHOLOGY SPECIMEN COLLECTION ADDRESSING NETWORK DISRUPTIONS AND DATA PRIVACY The present invention provides a system and method for pathology collection. A computer-implemented method for improved functioning of electronic pathology systems to overcome network disruptions is disclosed. This method encompasses receiving patient information and a list of requested medical tests, including the medical professional's identification, then determining suitable container types for the tests and generating a unique string encapsulating the test details, container indications, and patient information. The generated string comprising the decision tree indication and the scores assigned to each set of container indication. The generated string is further secured through encryption. Subsequently, a collector's device captures and processes this pattern, extracting and decrypting the necessary information using the corresponding public key. The container indications are presented to the collector, complete with images and quantities of required containers. A server and a computing device are also disclosed. Figure 1 Abstract SYSTEM AND METHOD FOR PATHOLOGY SPECIMEN COLLECTION ADDRESSING NETWORK DISRUPTIONS AND DATA PRIVACY The present invention provides a system and method for pathology collection. A computer-implemented method for improved functioning of electronic pathology systems to overcome network disruptions is disclosed. This method encompasses receiving patient information and a list of requested medical tests, including the medical professional's identification, then determining suitable container types for the tests and generating a unique string encapsulating the test details, container indications, and patient information. The generated string comprising the decision tree indication and the scores assigned to each set of container indication. The generated string is further secured through encryption. Subsequently, a collector's device captures and processes this pattern, extracting and decrypting the necessary information using the corresponding public key. The container indications are presented to the collector, complete with images and quantities of required containers. A server and a computing device are also disclosed. Figure 1 20 25 20 01 96 10 J an 2 02 5 A b s t r a c t S Y S T E M A N D M E T H O D F O R P A T H O L O G Y S P E C I M E N C O L L E C T I O N A D D R E S S I N G N E T W O R K D I S R U P T I O N S A N D D A T A P R I V A C Y 2 0 2 5 2 0 0 1 9 6 1 0 J a n 2 0 2 5 Fi gu re 1 C om pu tin g de vi ce R eg is te r f or a cc ou nt U pl oa d lis t o f o rd er ab le te st s St or ag e M ed ia Pa tie nt M ed ic al Pr of es si on al La bo ra to ry R ec ei ve te st li st R eg is te r f or ac co un t Se le ct a p at ie nt re co rd in C IS St ar t n ew pa th ol og y re qu es t R en de r in te rfa ce Q ue ry C IS fo r cu rre nt p at ie nt A Vi si t i n pe rs on o r vi rtu al ly B 10 2 10 0 10 4 20 0 20 2 20 4 20 6 30 0 30 2 30 4 40 0 Se rv er 10 1 / 9 20 25 20 01 96 1 0 Ja n 20 25 2025200196 10 Jan 2025 10 102 Computing Patient device 400 Visit in person or virtually 302 100 B Render interface A

The present invention relates to the field of educational technology, and in particular to a method for evaluating medical-preventive integration skills based on a causal-machine model, comprising the following steps: generating a question feature vector and a knowledge point semantic vector using natural language processing technology combined with a deep learning model, and calculating the correlation between the feature vectors using cosine similarity; generating a question difficulty coefficient based on the causal-machine model; generating a student knowledge mastery feature vector based on the student's answer accuracy and answering speed; recommending the most suitable homework questions to the student based on the deep learning model; and providing feedback and learning suggestions by quantitatively analyzing the student's answering performance through the causal-machine model. The present invention can identify students' weak links, effectively improve the efficiency of preventive medicine professional learning, assist teachers in making teaching decisions, and promote the personalized training of medical-preventive integration and medical-preventive collaborative talents.

Provided herein is an apparatus and method for ultrasound compatible ECMO training of medical professionals, specifically to train them in performing cannulation in real surgical scenarios. The apparatus can include a manikin for Extracorporeal Membrane Oxygenation (ECMO) training including: a body of the manikin formed to resemble at least a portion of a human torso; a closed-circuit circulation loop within the body; at least one cavity within the body proximate a location corresponding to a thigh; at least one removable gel insert received within the at least one cavity; a medial chest plate including one or more springs attached to a top side of the medial chest plate; and a top chest plate attached to the one or more springs opposite the medial chest plate, where a compartment is defined below the medial chest plate, and wherein the closed-circuit circulation loop is disposed at least partially within the cavity.

To prevent a medical worker who is a user from misunderstanding that there is some relation between medical articles displayed continuously.SOLUTION: The doctor terminal 30 is provided with an article moving part R201 for moving the medical article to be displayed in the article display area 31J corresponding to the operation of a user to the doctor terminal 30, a normal display part 31K for displaying the medical article in a normal state that the user can read the medical article and a visibility lowering processing part 31F for executing visibility lowering processing for lowering the visibility of the medical article more than the normal state to the medical article. The article display control part 31C displays the first medical article AT1 in a part of the article display area R201 in a normal state and displays the result of the visibility reduction processing executed to the second medical article R201 in another part of the article display area AT2 in the article transition stage.SELECTED DRAWING: Figure 4B

The invention relates to the technical field of medical education and training, in particular to an augmented reality (XR) medical learning system based on a Miller pyramid medical evaluation model. The system comprises a medical professional knowledge and operation theory layer, a medical knowledge application capability layer, a clinical operation presentation layer and a system evaluation and feedback mechanism, and provides a progressive learning path through technical means such as high-precision three-dimensional model generation, multi-person cross-space cooperation, real-time feedback and personalized learning. The system can significantly improve the learning intuition, interactivity and authenticity, solves the problems of limited resources, lack of systematicness and insufficient collaborative learning opportunities in traditional medical education, provides a comprehensive and systematic surgical operation training platform for medical students, and assists the comprehensive improvement of surgical skills and comprehensive quality.

The invention provides a medical field-oriented multi-modal large language model training and application method, which is used for solving the problems of insufficient speciality, nonstandard output, low information extraction accuracy and the like of an existing general model in medical data processing. The method comprises the following steps: firstly, converting medical data from different sources and modalities into image data in a unified format through unified processing; then, training the model by utilizing supervised fine tuning, injecting medical professional knowledge, and improving the adaptability of the model in medical data analysis; and finally, by introducing a reinforcement learning optimization model output format, it is ensured that the generated structured data conforms to a predetermined standardized JSON format. According to the method, the accuracy, the stability and the format compliance of the model in a medical information extraction task can be remarkably improved, the intelligent processing effect of medical data is improved, and direct docking with a hospital information system, an electronic medical record system and the like is realized.

Embodiments disclosed herein include software processes executed by a computer for ingesting and analyzing MRI image data and generating MRI-related reports. The report generation software executed by a computer generates MRI reports using medical data and MRI imagery data. The computer creates a text summary. Additionally, the computer generates the MRI report to include medical illustrations that overlay and merge with the MRI imagery data to visually explain the MRI results. Generative machine-learning models integrate colorized MRI images with the illustrations. The computer selects key MRI slices, colorizes them, matches them with relevant illustrations, and recursively refines the combined images to improve clarity and informational content. This process enhances the interpretability of radiology reports for both medical professionals and patients.

A method of determining a dose of a therapeutic composition to a patient is provided. The patient is wearing a wearable device configured to detect at least one physiological parameter of the patient. The method includes detecting the at least one physiological parameter of the patient. The method further includes transmitting data associated with the at least one physiological parameter of the patient to a medical professional. The method also includes determining the dose of the therapeutic composition based on the data associated with the at least one physiological parameter of the patient.

The application relates to a medication recommendation method and system, which comprises the following steps: preprocessing original medical data of a current patient into a standardized time sequence diagnosis and treatment sequence; inputting the time sequence diagnosis and treatment sequence and a medical professional knowledge base into a code-visit two-level hierarchical attention model for processing, obtaining code-level aggregated features of each visit and corresponding visit-level attention weights, weighting and aggregating the code-level aggregated features based on the visit-level attention weights, and generating a global health representation of the current patient; searching for similar historical cases as reference cases from a case information base based on at least one of a plurality of medical feature information of the current patient; and generating and outputting a medication recommendation list for the current patient based on at least the global health representation of the current patient and a medication scheme of the reference cases. The application can improve the accuracy and safety of medication recommendations for the current patient, thereby better assisting doctors in making medication decisions.

The invention aims to provide a medical intelligent interaction system based on double AI engines, and the system comprises a clinical diagnosis AI engine module which is configured to receive medical symptom data and generate diagnosis suggestions and treatment schemes; the drug research and development AI engine module is configured to analyze drug target data and predict drug properties and curative effects; the unified user interface interaction module is used for providing a medical specialized user interaction interface and an operation process; the multi-source medical data fusion module is used for integrating medical data from the PDBbind database, the medical evidence-based guide and the PubMed literature; the personalized learning path generation module is used for generating customized learning contents based on professional backgrounds and learning behaviors of the users; the system integration management module is used for coordinating data exchange and function collaboration among the modules; the clinical diagnosis AI engine module and the drug research and development AI engine module are based on the same basic deep learning model architecture, but respectively use a clinical medical data set and a drug research and development data set to carry out domain specific fine tuning training, and the two AI engine modules share a bottom feature extraction network but have independent professional output layers.

The quality of a chest X-ray depends on the inspiration state of the patient being imaged. If the patient's lungs are not significantly inflated during the chest X-ray, tissue from surrounding organs can obscure the image and the lung parenchyma is not effectively shown. Therefore, it is up to the medical professional to ensure that the patient is in the proper posture and inspiration state at the point of image exposure. But this can be difficult for patients with chronic conditions. Therefore, this application discusses a means of assessing the inspiration state using the position of the ribs and diaphragm line in the X-ray image. Thus, an accurate estimate of the inspiration state can be automatically reported to the medical professional before the patient leaves the examination room, allowing a retake of the X-ray if necessary.

An integrated system for specialized email services and precision targeted advertising is designed for healthcare professionals, facilitating targeted advertising based on verified medical specialties. The system includes a verification process that authenticates healthcare professionals' credentials and specialties, using this verified information to assign specialty-specific email addresses within a HIPAA-compliant email service. Advertisements are precisely tailored and delivered by utilizing these email addresses, supported by a sophisticated precision targeted advertising module that employs specialty information encoded in the email addresses and additional behavioral data from user interactions across various platforms, utilizing verified specialty records. This approach not only enhances the relevance and effectiveness of advertisements but also ensures compliance with healthcare privacy regulations, including HIPAA, safeguarding all data exchanges. The system aims to optimize advertising effectiveness by ensuring that healthcare professionals receive only the most relevant ads, thus boosting engagement rates and enhancing marketing efforts within the healthcare sector.

The invention belongs to the technical field of knowledge maps, and particularly relates to an intelligent customer service dynamic knowledge updating and answering method and system based on large model collaboration, and the method comprises the steps: obtaining a plurality of dialogue units comprising user input, customer service reply and medical entities; calculating a medical entity context retention index and a dialogue process retention index; performing weighted summation on the medical entity context retention index, the dialogue process retention index and the maximum similarity value to obtain the comprehensive context perception similarity; performing de-duplication processing based on the comprehensive context sensing similarity; clustering dialogue units of which the comprehensive context sensing similarity is greater than a high threshold value, and reserving one dialogue unit in each class; and marking the dialogue units of which the comprehensive context perception similarity is less than a high threshold and greater than or equal to a low threshold as clinically related variants, and reserving the clinically related variants. According to the method, the original context structure of the medical dialogue is kept, and the medical professional knowledge is combined to perform accurate duplicate removal.