Use case type
This category uses AI to analyze medical images such as scans or X-rays to identify patterns, abnormalities, or areas of interest. It helps support clinical review and improve consistency in image interpretation.
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29
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19 mo
Adoption over time
By case publish month · completed months only
20 cases documented across 37 months (Jul 23 – Jul 26), peaking at 6 in May 2026.
6 earlier cases before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
Company examples
10 shown from 29 use cases
FathomX used Alibaba Cloud Elastic Compute Service and Elastic GPU Service to train and deploy an AI breast cancer detection system.The company integrated its FxMammo model into a computer-aided detection and diagnosis workflow for radiologists and operated it in a secure, compliance-ready cloud environment.
EagleView uses aerial imagery and machine learning to provide insights for construction, real estate, insurance, emergency services, and energy customers. Its image-processing system must support large concurrent workloads and near real-time inference for use cases with tight SLAs.To address scaling and reliability challenges, EagleView migrated two ML pipelines from Amazon EKS-based infrastructure to Amazon SageMaker within eight months, standardizing deployment and using asynchronous inference and autoscaling to manage large request volumes.
MD.ai developed an annotation platform on Google Cloud leveraging Google Kubernetes Engine and Cloud Healthcare API for scalable, cloud-native medical dataset annotation to support AI research.The platform improved annotation efficiency, enabling 1,300+ teams to process 30,000+ medical images rapidly.Used in the RSNA Pneumonia Detection Challenge with 3,000+ participants, the platform supported advanced dataset annotation and collaborative AI model development.Cloud-native design removes the need for local apps, improving usability and collaboration for medical researchers and radiologists.
Ambra Health, a medical data and image management SaaS company, partnered with Google Cloud to develop a secure, scalable platform for anonymizing and sharing medical imaging data to accelerate AI research collaboration.The solution leverages Google Cloud Healthcare API, Cloud Data Loss Prevention (DLP) API, Compute Engine, and Cloud Storage to de-identify and manage sensitive patient imaging data at scale.A major academic medical center uses Ambra's cloud-native PACS solution on Google Cloud, enabling efficient anonymization, annotation, secure sharing, and web-based zero-footprint DICOM viewer access for researchers.This platform reduced cloud deployment onboarding time from months to minutes while meeting HIPAA and GDPR compliance, advancing deep learning and AI research with anonymized imaging data.
The American Cancer Society partnered with Slalom to accelerate and improve the accuracy of breast cancer image analysis for epidemiologic research.Using Google Cloud Cloud ML Engine, Cloud Storage, and TensorFlow, an end-to-end machine learning pipeline was developed for imaging preprocessing, training, and clustering.ML accelerated image analysis by 12 times, improved consistency and objectivity by removing human limitations, and enabled better understanding of breast cancer tissue patterns for future research.
ROKIT Healthcare personalizes and accelerates wound healing and organ regeneration treatments using AI and MLOps for global scalability.ROKIT developed ML models with Vertex AI to analyze 3D wound data from smartphone sensors, create personalized 3D bio-ink patches for skin and cartilage regeneration.They deployed an MLOps environment on Google Kubernetes Engine for seamless machine learning lifecycle management and global deployment.
Karkinos Healthcare developed a scalable, cloud-native oncology platform focused on early cancer detection and care for underinsured populations across India.The platform manages large genomic datasets, conducts low-cost, population-scale cancer risk assessments customized for diverse local languages and environments, and integrates partner clinics.The system processes over 10,000 genomics sequences yearly and serves over 400,000 users, enabling early cancer diagnosis with AI-powered machine learning for imaging analysis and NLP for clinical notes.Technology choices include Google Kubernetes Engine for automatic scaling, Cloud Healthcare API, Cloud Load Balancing, Google Cloud Armor for security, and Google Cloud Storage.Partner Persistent Systems supported infrastructure design and billing optimization.The solution helped onboard 70 partner clinics, facilitated patient access in remote regions, and provided upskilling for doctors to improve oncological care.
Philips uses AWS to accelerate AI solution development and reduce model training time, delivering faster innovation and improved patient outcomes.Collaborated with AWS to upskill over 5,000 employees through AWS Skill Builder for AI capabilities.Leveraged Amazon SageMaker AI ToolSuite to speed up ML development from weeks to days and integrated AI across healthcare workflows.Implemented data privacy automation and portable medical imaging solutions on AWS cloud, significantly improving operational efficiency and reducing MRI scan time.Deployed medical image analysis with AWS IoT and EKS enabling real-time processing and cost reductions.
Nous leverages Microsoft Azure OpenAI to advance AI use for medical imaging, disease prediction, and drug discovery in the healthcare sector. Their tailored solutions enhance efficiency and enable powerful insights for diagnostic and operational tasks.
Philips, leveraging a partnership with icometrix, has integrated AI-driven imaging and reporting solutions into its latest BlueSeal MR scanners and healthcare informatics platform to address the growing demand for neurological diagnosis and monitoring in the Netherlands and beyond. The solution employs icometrix’s AI-powered quantitative reporting software and Philips’ cloud-based AI Manager running on Microsoft Azure. With the introduction of novel Alzheimer’s therapies and an expanding patient population, the automated platform delivers fast, accurate, and scalable MRI analysis for Alzheimer’s and multiple sclerosis (MS). High diagnostic consistency and automation ease the burden on neuroradiologists and enable personalized treatments. The platform integrates into radiology workflows, supports FDA/CPT III reimbursement in the US, and features protocols to automate labor-intensive evaluation steps, allowing clinicians to address both Alzheimer’s and MS with improved confidence and efficiency.
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