You are currently viewing COMPREHENSIVE GLOBAL MEDICAL CLOUD AI PLATFORMS DIRECTORY
COMPREHENSIVE GLOBAL MEDICAL CLOUD AI PLATFORMS DIRECTORY

COMPREHENSIVE GLOBAL MEDICAL CLOUD AI PLATFORMS DIRECTORY

COMPREHENSIVE GLOBAL MEDICAL CLOUD AI PLATFORMS DIRECTORY

🇺🇸 UNITED STATES

1. Aidoc

Website: https://www.aidoc.com

What It Detects:

  • Acute conditions: strokes, pulmonary embolisms, intracranial hemorrhages
  • Cervical spine fractures
  • Rib fractures
  • Pneumothorax
  • Critical findings in CT and MRI scans

Key Features: FDA-cleared AI platform with 1,600+ hospital deployments globally. Built on aiOS™ platform for real-time intelligence integration.

Doctor/Patient Experiences: “Aidoc’s AI tools are winning over radiologists by prioritizing critical cases and improving diagnostic accuracy. The platform seamlessly integrates into existing workflows, allowing us to focus on complex cases while AI handles initial screening.” – Radiology Department testimonial

Aidoc Customer Stories

Doctor using AI radiology platform


2. PathAI

Website: https://www.pathai.com

What It Detects:

  • Cancer diagnosis from tissue samples
  • Pathology image analysis
  • Tumor classification and grading
  • Biomarker identification

Key Features: AI-assisted pathology using machine learning for more accurate cancer diagnosis and treatment planning.

AI pathology imaging


3. Viz.ai

Website: https://www.viz.ai

What It Detects:

  • Stroke detection and care coordination
  • Pulmonary embolism
  • Large vessel occlusions
  • Aortic dissection

Key Features: Trusted by 1,700+ hospitals, AI-powered care coordination platform that moves beyond diagnostics to streamline critical care workflows.


4. Zebra Medical Vision (acquired by Nanox)

Website: https://www.nanox.vision

What It Detects:

  • Cardiovascular conditions
  • Liver and lung diseases
  • Bone health (osteoporosis)
  • Compression fractures

Key Features: AI-powered radiology solutions analyzing X-rays, CTs, and MRIs for faster diagnostic readings.


5. Google Health AI

Website: https://health.google

What It Detects:

  • Diabetic retinopathy (90%+ accuracy)
  • Breast cancer from mammograms
  • Lung cancer screening
  • Tuberculosis detection

Key Features: Deep learning models for medical imaging with research partnerships across Apollo Radiology International and other healthcare providers.

Experience Notes: Initial clinical deployment in Thailand showed challenges with real-world implementation, leading Google to refine user interface and workflow integration.

Medical AI diagnosis platform


6. IBM Watson Health (Now part of various acquisitions)

Website: https://www.ibm.com/watson-health

What It Detects:

  • Oncology treatment recommendations
  • Drug interactions
  • Patient risk assessment
  • Clinical decision support

Experience Notes: Watson for Oncology initially faced challenges with overpromising capabilities. However, specific applications like genomic analysis showed value in controlled settings. The platform has been reorganized with various components sold to different healthcare companies.


7. Microsoft Nuance Healthcare AI

Website: https://www.nuance.com/healthcare.html

What It Detects:

  • Clinical documentation automation
  • Radiology reporting assistance
  • Virtual care support
  • Ambient clinical intelligence

8. Amazon HealthLake & AWS Healthcare AI

Website: https://aws.amazon.com/health

What It Detects:

  • Medical data aggregation and analysis
  • Predictive analytics for patient outcomes
  • Natural language processing for clinical notes

🇪🇺 EUROPE

9. Siemens Healthineers AI

Website: https://www.siemens-healthineers.com/digital-health-solutions

What It Detects:

  • Comprehensive imaging diagnostics (CT, MRI, X-ray)
  • Cardiac imaging analysis
  • Lung nodule detection
  • Automated organ segmentation

Key Features: Enterprise imaging IT with AI-powered decision support across the care continuum.

Doctor Experience: “AI is helping doctors see patterns in complexity, hospitals use resources more wisely, and patients receive care that’s as personal as it is powerful.” – Clinical testimonial from Siemens Healthineers perspective article

AI medical imaging platform


10. Philips Healthcare AI

Website: https://www.philips.com/healthcare/solutions/artificial-intelligence

What It Detects:

  • Cardiovascular diagnostics
  • Ultrasound AI analysis
  • Sleep and respiratory care
  • Oncology imaging

Key Features: Generative AI in healthcare, portable AI-powered ultrasound, and integrated clinical workflow solutions.

Healthcare professional using AI diagnostics


11. Aiforia (Finland)

Website: https://www.aiforia.com

What It Detects:

  • Digital pathology analysis
  • Tissue image quantification
  • Cancer research applications
  • Histopathology automation

Key Features: Available on Google Cloud Marketplace, serving clinical software solutions for AI-powered pathology.


12. Kheiron Medical (UK)

Website: https://www.kheironmed.com

What It Detects:

  • Breast cancer screening from mammograms
  • Earlier detection of subtle abnormalities

🇷🇺 RUSSIA

13. MosMedAI (Moscow AI Platform)

Website: https://ai.mosgorzdrav.ru

What It Detects:

  • Osteoporosis
  • Breast cancer
  • Pneumonia and respiratory diseases
  • Lung cancer symptoms from CT scans
  • COVID-19 diagnosis
  • Cardiomegaly

Key Features: Launched in 2024 with 17 AI services for healthcare professionals. Used by doctors across 72 regions of Russia.

Doctor Experience: “Moscow’s AI platform has significantly reduced diagnostic time for lung cancer detection in CT scans. The system flags suspicious findings immediately, allowing us to initiate treatment protocols faster.” – Moscow Department of Health report

CT scan AI analysis


14. Webiomed

Website: https://webiomed.ru/en

What It Detects:

  • Patient risk assessment
  • Disease prediction algorithms
  • Complication forecasting
  • Treatment outcome predictions

Key Features: First AI-based Russian software officially registered as medical device. Analyzes de-identified medical data from electronic health records.

Experience: “Webiomed system helps identify high-risk patients automatically, enabling proactive intervention. The predictive analytics have been valuable in preventing disease progression.” – Russian healthcare provider feedback


15. RADLogics (via Yandex Cloud)

Website: Integration with Yandex Cloud platform

What It Detects:

  • Lung pathologies
  • Cardiomegaly
  • COVID-19
  • Pneumothorax
  • Automatic aortic measurements

Doctor examining X-ray with AI


🇮🇳 INDIA

16. Qure.ai

Website: https://www.qure.ai

What It Detects:

  • Tuberculosis (TB) screening
  • Lung cancer detection
  • Stroke identification (qER, qSCORE)
  • Chest X-ray abnormalities (qXR)
  • Head CT analysis (qCT)

Key Features: Deployed across India for TB screening programs, working with government health initiatives.

Doctor Testimonials: “Qure.ai’s chest X-ray solution helps in early identification of probable TB cases and helps doctors in fast-tracking of TB patients for confirmatory testing. The turnaround time has decreased significantly.” – Featured customer testimonial

Patient Impact: Real-world evidence shows AI-powered hub-and-spoke stroke care models have enhanced treatment accessibility in rural India.

Qure.ai Customer Reviews

AI chest X-ray analysis platform


17. DeepTek.ai

Website: https://www.deeptek.ai

What It Detects:

  • Tuberculosis screening
  • COVID-19 detection
  • Chest X-ray abnormalities
  • Comprehensive radiology diagnostics

Key Features: Servicing 500+ hospitals and imaging centers across India and APAC. Provides workflow optimization and central reporting services.

Impact Stories: “DeepTek’s mission to make quality radiology services affordable and accessible is transforming healthcare delivery in tier-2 and tier-3 cities. AI-enabled diagnosis reports with remote accessibility have increased pathologist efficiency.” – DeepTek Impact Stories

DeepTek Impact Stories


18. SigTuple

Website: https://sigtuple.com

What It Detects:

  • Blood cell analysis
  • Urine microscopy
  • Peripheral blood smear analysis
  • Remote pathology diagnostics

Key Features: AI100 platform can be deployed in Tier-2 cities for remote diagnostic accessibility.


🇮🇩 INDONESIA

19. Indonesia.md

Website: https://indonesia.md

What It Detects:

  • Advanced cardiac care monitoring
  • Post-implant care for heart devices
  • Medical device integration diagnostics

Key Features: Developing AI agents to support post-implant care, linking Indonesian hospitals with regional specialists. Converging advanced medtech with medical cloud platforms.


20. Google Cloud SATUSEHAT (Indonesia Ministry of Health)

Platform: National health data exchange platform

What It Detects:

  • Population health analytics
  • Disease surveillance
  • Healthcare resource optimization

Key Features: Google Cloud collaboration exploring AI applications in Indonesia’s national health infrastructure.

Healthcare technology interface


🇸🇬 SINGAPORE

21. HMI Medical AI Platform

Website: https://www.hmimedical.com

What It Detects:

  • Non-clinical operations optimization
  • Healthcare workflow management
  • Patient care coordination

Key Features: Operating across Singapore, Malaysia, and Indonesia on unified cloud-based Workday AI platform.


22. National AI Innovation Challenge (Singapore)

Platform: Government healthcare initiative

What It Detects:

  • Chronic disease management (targeting 1.8 million residents)
  • Diabetes complications
  • Hypertension monitoring
  • Preventive care optimization

Key Features: AI Innovation Challenge 2026 launched to tackle chronic diseases through smart healthcare solutions.


🇨🇳 CHINA

23. Tencent Miying (腾讯觅影)

Website: https://miying.qq.com

What It Detects:

  • Esophageal cancer (90% accuracy)
  • Lung nodules and lung cancer
  • Colorectal polyps
  • Diabetic retinopathy
  • Cervical cancer (electronic colposcopy)
  • Breast cancer

Key Features: One of China’s first medical AI platforms with extensive hospital deployment across the country.

Doctor Experience: “Tencent Miying’s AI Electronic Colposcopy-Aided Diagnosis system helps locate polyps and quantify risk during colonoscopy procedures. The detection process has become significantly faster and more standardized.” – Hospital testimonial from CGTN news

Tencent Miying medical AI platform


24. Alibaba Cloud ET Medical Brain

Website: https://www.alibabacloud.com/solutions/intelligence-brain/medical

What It Detects:

  • Cancer screening and detection
  • Medical imaging analysis (CT, MRI)
  • Electronic health record intelligence
  • Acute aortic syndrome (iAorta – diagnosis in seconds)
  • 12 common medical disciplines at “Deputy Chief Physician” standard

Key Features: “Doctor You” intelligent diagnostics solution for early disease detection. ET Medical Brain achieved 74.8% accuracy at specialist-level medical exams.

Clinical Impact: “With iAorta, doctors can use routine, non-contrast CT scans to identify acute aortic syndrome in seconds, cutting diagnosis time to under a minute. This has been transformative for emergency care.” – Yicai Global report

Alibaba ET Medical Brain AI diagnosis


25. Baidu Medical AI (Baidu Brain)

Website: https://ai.baidu.com

What It Detects:

  • Remote consultation diagnostics
  • Medical image recognition
  • Disease prediction models
  • Natural language processing for medical queries

26. Ping An Good Doctor AI

Website: https://www.pagd.net

What It Detects:

  • Online consultation triage
  • Symptom assessment
  • Prescription assistance
  • Health management

27. iFlytek Medical AI

Website: https://www.iflytek.com

What It Detects:

  • Medical imaging diagnostics
  • Clinical decision support
  • Voice recognition for medical documentation

28. Shanghai Medical AI (Featured at Pujiang Conference)

Platform: Hospital-integrated AI systems

What It Detects:

  • Trained on 30,000+ medical cases
  • Comprehensive diagnostic capabilities across multiple specialties

Performance: Shanghai-built medical AI demonstrated competitive accuracy against human physicians in diagnostic challenges.

AI hospital technology China


ADDITIONAL NOTABLE PLATFORMS

29. Butterfly Network (Portable AI Ultrasound)

Website: https://www.butterflynetwork.com

  • Portable AI-powered ultrasound devices
  • Point-of-care diagnostics
  • Cloud-based image analysis

30. Tempus (Precision Medicine – USA)

Website: https://www.tempus.com

  • Oncology precision medicine
  • Molecular data analysis
  • Personalized cancer treatment

KEY OBSERVATIONS FROM PATIENT & DOCTOR EXPERIENCES

Positive Impacts Reported:

  1. Faster Diagnosis: AI reduces interpretation time from hours to minutes
  2. Early Detection: Higher accuracy in detecting early-stage cancers and critical conditions
  3. Workflow Efficiency: Radiologists can prioritize critical cases flagged by AI
  4. Rural Healthcare Access: Telemedicine integration with AI brings specialist-level diagnostics to underserved areas
  5. Reduced Burnout: Administrative automation allows doctors to focus on patient care

Challenges Noted:

  1. Trust Gap: 34% more clinicians see AI benefits than patients (2025 FHI report)
  2. Integration Issues: Real-world deployment requires careful workflow adaptation
  3. Training Needs: Healthcare staff require training to effectively utilize AI tools
  4. Data Privacy: Concerns about patient data security in cloud-based systems

Medical professional with AI diagnostic screen


FUTURE TRENDS (2026 Outlook)

Healthcare AI is shifting from generative content to agentic AI – intelligent agents capable of executing complex clinical tasks autonomously. Key developments include:

  • Clinical copilots for real-time decision support
  • Remote monitoring with predictive analytics
  • Multimodal AI combining imaging, genomics, and clinical data
  • Blockchain integration for secure health data exchange
  • Virtual hospitals powered by AI infrastructure

Futuristic AI healthcare integration


This comprehensive directory represents the current landscape of medical cloud AI platforms globally. Each platform continues to evolve with new capabilities, expanding their detection scope and improving accuracy through ongoing machine learning improvements and clinical validation studies.

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