AlRyalat et al., 2024 - Google Patents
Evaluating the strengths and limitations of multimodal ChatGPT-4 in detecting glaucoma using fundus imagesAlRyalat et al., 2024
View HTML- Document ID
- 5529965750566262969
- Author
- AlRyalat S
- Musleh A
- Kahook M
- Publication year
- Publication venue
- Frontiers in Ophthalmology
External Links
Snippet
Overview This study evaluates the diagnostic accuracy of a multimodal large language model (LLM), ChatGPT-4, in recognizing glaucoma using color fundus photographs (CFPs) with a benchmark dataset and without prior training or fine tuning. Methods The publicly …
- 208000010412 Glaucoma 0 title abstract description 56
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/324—Management of patient independent data, e.g. medical references in digital format
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
- G06F17/30601—Clustering or classification including cluster or class visualization or browsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders | |
| US11182550B2 (en) | Cognitive building of medical condition base cartridges based on gradings of positional statements | |
| US10607736B2 (en) | Extending medical condition base cartridges based on SME knowledge extensions | |
| CN103069423B (en) | Systems and methods for assisting report authoring | |
| AlRyalat et al. | Evaluating the strengths and limitations of multimodal ChatGPT-4 in detecting glaucoma using fundus images | |
| US11048874B2 (en) | Medical record error detection system and method | |
| US10971254B2 (en) | Medical condition independent engine for medical treatment recommendation system | |
| US20120301864A1 (en) | User interface for an evidence-based, hypothesis-generating decision support system | |
| Cai et al. | Clinical correlates of computationally derived visual field defect archetypes in patients from a glaucoma clinic | |
| Rojas-Carabali et al. | Chatbots vs. human experts: evaluating diagnostic performance of chatbots in uveitis and the perspectives on AI adoption in ophthalmology | |
| CN114201613B (en) | Test question generation method, test question generation device, electronic device, and storage medium | |
| Barnard et al. | Self-diagnosis and large language models: A new front for medical misinformation | |
| Ho et al. | Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review | |
| Laurik-Feuerstein et al. | The assessment of fundus image quality labeling reliability among graders with different backgrounds | |
| Anguita et al. | Assessing large language models’ accuracy in providing patient support for choroidal melanoma | |
| Luk et al. | Performance of GPT-4 and GPT-3.5 in generating accurate and comprehensive diagnoses across medical subspecialties | |
| Zhao et al. | Slit lamp report generation and question answering: development and validation of a multimodal transformer model with large language model integration | |
| Van Eijgen et al. | Leuven-haifa high-resolution fundus image dataset for retinal blood vessel segmentation and glaucoma diagnosis | |
| Chng et al. | Application of artificial intelligence in the assessment of thyroid eye disease (TED)-a scoping review | |
| Šuto Pavičić et al. | Using ChatGPT to improve the presentation of plain language summaries of Cochrane systematic reviews about oncology interventions: cross-sectional study | |
| US20190197135A1 (en) | Intelligently Organizing Displays of Medical Imaging Content for Rapid Browsing and Report Creation | |
| Das et al. | AI chatbots in answering questions related to ocular oncology: a comparative study between DeepSeek v3, ChatGPT-4o, and Gemini 2.0 | |
| Taloni et al. | Large language models provide discordant information compared to ophthalmology guidelines | |
| Srinivasan et al. | Benchmarking llms for ophthalmology (belo) for ophthalmological knowledge and reasoning | |
| Sensoy et al. | Assessing the competence of artificial intelligence programs in pediatric ophthalmology and strabismus and comparing their relative advantages |