A.I. in Health

  1. IBM Watson Health: IBM's AI system is used for various tasks like cancer diagnosis, drug discovery, and population health management. Effectiveness varies depending on application. Watson for Oncology has shown mixed results in assisting oncologists in treatment recommendations, with some studies reporting improved concordance with expert opinions and others highlighting limitations in interpretation.
  2. Google DeepMind Health (now part of Google Health): DeepMind's AI has been applied to tasks such as predicting acute kidney injury, analyzing retinal scans, and improving radiotherapy planning. In a study published in Nature, their AI system showed high accuracy in detecting eye diseases comparable to ophthalmologists.
  3. Zebra Medical Vision: Zebra's AI algorithms analyze medical images for early detection of conditions like breast cancer, lung disease, and bone fractures. Their systems have been validated through multiple clinical studies, demonstrating high accuracy and potential to enhance diagnostic efficiency.
  4. Aidoc: Aidoc's AI solutions focus on prioritizing and flagging urgent cases in medical imaging, such as detecting intracranial hemorrhages and pulmonary embolisms. Clinical trials have shown that Aidoc's algorithms can significantly reduce turnaround times and improve patient care.
  5. PathAI: PathAI uses AI for analysis, assisting pathologists in diagnosing diseases like cancer. Their technology has demonstrated improved accuracy in identifying tumor subtypes and predicting patient outcomes.
  6. Your.MD (Now part of Babylon Health): This AI-powered chatbot provides personalized health advice and triage services. While it can help with minor health concerns, it's not intended as a substitute for professional medical advice. User satisfaction is generally positive, but clinical validation for more severe conditions is limited.
  7. Enlitic: Enlitic develops AI tools for medical image analysis. Their technology has shown promise in improving diagnostic accuracy and efficiency.

  1. MONAI (Medical Open Network for AI): MONAI is an open-source framework specifically designed for medical imaging applications. It is built on top of PyTorch and provides tools, libraries, and workflows for training, validating, and deploying AI models in medical imaging.
  2. MIMIC-III and MIMIC-IV: These are open-access databases containing de-identified health-related data for research purposes, including intensive care unit (ICU) patient records. They are not AI systems but can be used to develop and train AI models.
  3. Cancer Genome Atlas (TCGA): TCGA is a public resource that provides genomic and clinical data for various cancer types. Researchers can use this data to develop AI models for cancer prediction and analysis.
  4. OpenI: OpenI is an open-source platform for medical image retrieval and analysis. It provides a large collection of annotated medical images and tools for image analysis.

A.I. In Reasearch

  1. AlphaFold by DeepMind: a specific deep learning model developed by DeepMind that predicts protein structures.
  2. Insilico Medicine: focuses on drug discovery and development using AI. Their platforms predict drug targets, generate new molecules, and assess their safety and efficacy.
  3. Atomwise: uses AI to screen virtual libraries of small molecules for potential therapeutic properties against specific protein targets.
  4. DeepChem: an open-source library for cheminformatics and drug discovery. It provides tools for data preprocessing, modeling, and analysis, enabling researchers to apply deep learning techniques to chemical and biological problems.
  5. BenchSci: an AI platform that helps researchers identify the most suitable antibodies for their experiments by analyzing millions of scientific papers.
  6. Pathway Studio: uses AI to analyze and visualize biological pathways, helping researchers understand disease mechanisms and identify potential drug targets.
  7. GenoLogics (now part of PerkinElmer): AI-powered laboratory information management system (LIMS) streamlines sample tracking, data management, and workflow automation in genomics and proteomics labs.
  8. Gero: applies AI to aging research and drug discovery, identifying potential anti-aging compounds and studying age-related diseases.
  9. IDx-DR: an AI-based system approved by the FDA for detecting diabetic retinopathy. It analyzes retinal images and provides a diagnosis without requiring a specialist to interpret the images.
  10. Freenome: developing an AI-driven liquid biopsy platform for early cancer detection.
  11. Owkin: combines AI and federated learning to analyze medical data across multiple institutions while preserving privacy.
  12. Cytora: uses AI to analyze large amounts of biomedical literature and data to identify novel drug targets and repurpose existing drugs for new indications.
  13. Cell Painting: a high-content screening method that combines AI with image analysis to classify cells based on their morphological features.
  14. NVIDIA Clara: an AI platform for healthcare and life sciences, offering tools for medical imaging, genomics, and other research areas.
  15. Kheiron Medical Technologies: uses AI to assist radiologists in mammography screenings, detecting breast cancer with high accuracy. Their system, Mia, has been clinically validated and is currently used in several countries.

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