md.ai

AI-powered reporting and annotation for radiologyAccelerate the development and deployment of medical imaging AI with DICOM-native data annotation tools. Supercharge your reporting workflows with AI and unlock extra efficiency and productivity.Get a DemoREPORTINGLeapfrog into the future of medical AI workflowsSupercharge clinical reporting workflows with LLMs to unlock extra efficiency and productivity. Automatic template selection, key findings dictation mapping, impression generation, proofreading, and more. Streamline administrative tasks with automated billing code generation. Improve patient communication and education with patient-friendly audio messages.Simple HL7/DICOM integration with EHR/HIS/RISWorks on desktop, laptop, tablet, or mobileMulti-device syncAI-driven or traditional reporting modesMultilingual supportContextual AI chatLearn MoreANNOTATORBuild high-quality datasets and modelsOur platform has enabled thousands of doctors and their collaborators to create large high-quality labeled datasets, deploy and validate their models, and build AI-driven clinical workflows.Native DICOM supportFDA 510(k)-cleared viewerSeamless scalingAI-assisted annotationPHI detection and De-IDDeveloper APIsLearn MoreContact UsQuestion for MD.ai? Send a message: email us at hello@md.ai, or fill out the form below.Your NameYour EmailYour Message --- REPORTINGLeapfrog into the future of medical AI workflowsSupercharge clinical reporting workflows with LLMs to unlock extra efficiency and productivity. Automatic template selection, key findings dictation mapping, impression generation, proofreading, and more. Streamline administrative tasks with automated billing code generation. Improve patient communication and education with patient-friendly audio messages.Simple HL7/DICOM integration with EHR/HIS/RISWorks on desktop, laptop, tablet, or mobileMulti-device syncAI-driven or traditional reporting modesMultilingual supportContextual AI chatFeaturesSmart Key Findings Mode: Multistep report construction with a single clickTemplate Auto-SelectionAutomatically selects the appropriate template based on the context and type of study, enhancing efficiency and accuracy.Key Findings MappingMaps key findings accurately and seamlessly, reducing the need for manual input and minimizing errors.Auto Impression GenerationGenerates impressions automatically, saving time and ensuring consistency across reports.Auto Insert GuidelinesInserts user-defined guidelines and recommendations directly into the report, ensuring compliance with best practices and improving report quality.Customization CapabilitiesOffers full customization options for templates, enabling the handling of complex cases and unique reporting needs that other products do not offer.Advanced AI capabilitiesAuto-compare prior reportsAutomatically compares changes in current findings with prior reports to identify significant differences and trends.Apply clinical guidelinesAutomatically apply selected clinical guidelines such as BI-RADS, LI-RADS, TI-RADS, and more to the report. Easily manage your own custom guidelines.Multilingual SupportGlobal ReachTested and utilized by users worldwide, ensuring robust and reliable performance across different languages and regions.Support for 12 LanguagesProvides comprehensive multilingual support, including real-time translation and transcription, localization, and audio patient messages, enhancing accessibility and usability for a diverse user base.Customizable Signing WorkflowAuto-Generate Billing CodesAutomatically generates billing codes upon signing a report, streamlining administrative processes and reducing manual errors.ProofreadingProofreads the report to ensure accuracy and completeness before finalizing, enhancing the quality of the reports.Reporting Routing and SharingReports can be sent to multiple destinations simultaneously in various formats, such as HL7, DICOM, PDF, and email. You can even send patient-friendly audio reports directly to patients.Customizable Signing WorkflowAllows customization of the signing workflow based on user preferences, providing flexibility and adapting to individual radiologists’ needs.Use Your Favorite Input DeviceKeyboard Shortcuts and Voice CommandsLots of keyboard shortcuts and voice commands are available to streamline your reporting workflow.Synchronized Mobile DeviceYour mobile device can be used as a synchronized voice input device.Philips SpeechMike IntegrationWe also support industry-standard dictation microphones like the Philips SpeechMike, with on-device key mappings for important functionality.Additional Key AdvantagesMobile OptimizationEnhances flexibility and convenience by eliminating the need for external microphones, providing a cost-effective solution.Advanced Chat CapabilitiesUtilizes HIPAA-compliant LLMs for various advanced tasks such as report analysis, interpretation, and answering FAQs for patients and healthcare providers.Seamless clinical integration with DICOM and HL7Our reporting software is a turnkey solution, requiring no personalized data or model training to deliver effective results. It’s ready to use straight out of the box, eliminating the need for any complex setup or customization.Enhanced Compliance and SecurityHIPAA ComplianceEnsures patient privacy and data security.Real-Time Compliance MonitoringSentry alerts on all services, system logging, and anomaly detection for enhanced compliance and security.Regular Third-Party AuditingContinuing 12-month third-party audit cycles with real-time compliance auditing, ensuring ongoing adherence to the highest standards of security and data integrity.Contact UsQuestion for MD.ai? Send a message: email us at hello@md.ai, or fill out the form below.Your NameYour EmailYour Message --- ANNOTATORBuild high-quality datasets and modelsOur platform has enabled thousands of doctors and their collaborators to create large high-quality labeled datasets, deploy and validate their models, and build AI-driven clinical workflows.Native DICOM supportFDA 510(k)-cleared viewerSeamless scalingAI-assisted annotationPHI detection and De-IDDeveloper APIsFeaturesDICOM nativeOur platform is built from the ground up to support the DICOM standard. We support most DICOM imaging modalities. Datasets can be created through direct uploads, connected via cloud storage buckets, or pushed via DICOM C-STORE protocol. While DICOM is our primary format, we can also ingest non-DICOM images (JPEG, PNG, TIFF) and videos (MP4, AVI, MOV) in custom patient-centric file structures.FDA 510(k)-cleared viewerOur FDA 510(k)-cleared, web-based DICOM viewer enables clinical image interpretation, review, annotation, and reporting. It supports most modalities, standard zoom/pan/windowing, hanging protocols, multiplanar reconstruction, measurement tools, and more. The viewer is fully integrated with our annotation tools.Easily scale to massive datasetsAutoscaling cloud infrastructure allows seamless scaling to millions of exams, terabytes of data, and thousands of real-time concurrent users. Our user management system provides fine-grained data access control and distributed labeling task assignments. We have helped build some of the largest public datasets in medical imaging through expert medical crowdsourcing.Deploy models for AI-assisted annotation or federated validationDeploy models and run distributed inference on your data. Use models for pre-annotation or AI-assisted annotation. Validate models across multiple sites in a federated manner without data sharing.Built-in AI toolsUtilize built-in AI-powered mask segmentation tools for more efficient annotation. We also offer built-in PHI detection and pixel and metadata deidentification tools to prevent sensitive data leakage.Flexible APIs for developersEnable programmatic project management and control via our CLI tool and Python client library.Demo VideoOverview of MD.ai Annotator: Key Features and FunctionalitiesContact UsQuestion for MD.ai? Send a message: email us at hello@md.ai, or fill out the form below.Your NameYour EmailYour Message --- About MD.aiMD.ai was founded by Harvard/Duke/Columbia-trained doctors to accelerate the application of AI in medicine, with a particular focus on medical imaging and software tools. With our data annotation platform, we help doctors, scientists, and engineers build high-quality datasets to train and validate AI models. With our AI-powered clinical reporting software, we give radiologists superpowers.We care deeply that our software and platform have the potential to not only boost the efficiency and productivity of healthcare providers, but also improve patient care and outcomes as well. This commitment drives our mission and propels us forward.Our software is used within top academic medical institutions as well as large pharmaceutical and healthcare companies.Recent ProjectsShowcase of selected public projects and datasetsRSNA 2024 Lumbar Spine Degenerative ClassificationClassify lumbar spine degenerative conditions.Kaggle CompetitionRSNA 2023 Abdominal Trauma DetectionDetect and classify traumatic abdominal injuries.Kaggle CompetitionDataset ArticleRSNA 2022 Cervical Spine Fracture DetectionIdentify cervical fractures from scans.Kaggle CompetitionDataset ArticleUNIFESP X-ray Body Part Classifier CompetitionCan you build an algorithm to correctly classify body parts in X-rays?Kaggle CompetitionSIIM-FISABIO-RSNA COVID-19 DetectionIdentify and localize COVID-19 abnormalities on chest radiographs.Kaggle CompetitionDataset ArticleRANZCR CLiP - Catheter and Line Position ChallengeClassify the presence and correct placement of tubes on chest x-rays to save lives.Kaggle CompetitionRSNA STR Pulmonary Embolism DetectionThe 2020 RSNA Pulmonary Embolism Detection Challenge invited researchers to develop machine-learning algorithms to detect and characterize instances of pulmonary embolism (PE) on chest CT studies. The competition, conducted in collaboration with the Society of Thoracic Radiology (STR), involved creating the largest publicly available annotated PE dataset, comprised of more than 12,000 CT studies. Imaging data was contributed by five international research centers and labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.Kaggle CompetitionDataset ArticleRSNA Intracranial Hemorrhage DetectionThe dataset for this Kaggle challenge was created on the MD.ai platform in collaboration with the Radiological Society of North America (RSNA) and the American Society of Neuroradiology (ASNR), with data contributions from Stanford University, St. Michael's Hospital, Thomas Jefferson University, and Universidade Federal de São Paulo.Kaggle CompetitionMD.ai ProjectSIIM-ACR Pneumothorax SegmentationThe dataset for this Kaggle challenge was created on the MD.ai platform in collaboration with the Society for Imaging Informatics in Medicine (SIIM) and the Society of Thoracic Radiology (STR).Kaggle CompetitionMD.ai ProjectRSNA Pneumonia Detection ChallengeThe dataset for this Kaggle challenge was created on the MD.ai platform in collaboration with the Radiological Society of North America (RSNA) and the Society of Thoracic Radiology (STR).Kaggle CompetitionMD.ai ProjectWhite Papers & PublicationsSelected white papers and publications from our teamEvaluating GPT-4V (GPT-4 with Vision) on Detection of Radiologic Findings on Chest RadiographsThis study examined the application of GPT-4 with vision (GPT-4V), a multimodal large language model with visual recognition, in detecting radiologic findings from a set of 100 chest radiographs and suggests that GPT-4V is currently not ready for real-world diagnostic usage in interpreting chest radiographs.ViewLessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI ChallengesOrganizing AI competitions for medical imaging, such as those by the RSNA, involves complex dataset creation and curation, addressing patient privacy, and ensuring data quality and consistency. These competitions foster global collaboration, advance research, and have the potential to transform healthcare by improving diagnostic accuracy and patient outcomes.ViewImproving model fairness in image-based computer-aided diagnosisDeep learning models for medical image classification can match or exceed clinician performance but may amplify human biases, leading to inaccuracies. This study introduces an algorithm to enhance model fairness using marginal pairwise equal opportunity, showing a 35% reduction in fairness disparity without significantly impacting overall performance across four large-scale cohorts.ViewEvaluating GPT-4 on Impressions Generation in Radiology ReportsIn this study, we systematically examined the capabilities and limitations of GPT-4 in performing zero-shot generation of Impressions from radiology report Findings. We evaluated the performance of GPT-4 against radiologist-generated Impressions along several predefined dimensions in our previous works—-coherence, comprehensiveness, factual consistency, and harmfulness—-to provide new insights into the feasibility of using large language models in radiology report generation and summarization.ViewChatGPT and Other Large Language Models Are Double-edged SwordsThis editorial provides several use cases to illustrate the potential role of these tools in the clinical setting and their potential effects on medical journalism.ViewValidating AI Models Collaboratively with NVIDIA Clara Imaging and MD.aiIn this post, we highlighted key components of each platform and the steps necessary to quickly deploy a medical imaging model built with NVIDIA Clara on MD.ai.ViewWhite Paper: RSNA Pneumonia Detection ChallengeDescription of the pneumonia crowdsourcing project and adjudication process.ViewWhite Paper: Crowdsourcing AnnotationsDescription of crowdsourced annotations for the RSNA Pneumonia Challenge.ViewGoogle Cloud Platform Case StudyMD.ai leverages Google Cloud and Cloud Healthcare API to create annotated datasets and build algorithms for machine learning to bring better insights to medical providers.View