ScienceIO transforms unstructured healthcare text into structured, actionable data using AI, covering over 9 million healthcare concepts. Use cases include patient cohorting, diagnostics, biomarker identification, and other digital health services.

emtelligent is a healthcare AI language solution, trained on billions of data points and 2M+ hand annotations. It uses NLP and medically-aligned LLMs to structure medical text and unlock insights at enterprise scale.

Claude 3 is an LLM that is utilized to automate tasks like generating personalized treatment plans and summarizing medical research. It is also used to analyze large volumes of patient data, helping to create tailored healthcare solutions. The model's capabilities include supporting drug discovery, clinical research, and patient care automation.

GenHealth.ai’s Large Medical Model (LMM) is designed to predict future healthcare events by analyzing sequences of medical events such as diagnoses, treatments, and medications. Instead of using traditional language models, it operates on clinical data like ICD, CPT, RxNorm, and LOINC codes, which represent patient conditions, procedures, and medications. The model processes these event sequences to forecast outcomes like healthcare costs, risks, and likely future medical procedures, helping healthcare providers anticipate patient needs and manage resources more effectively.

Gradient AI's large language models (LLMs) automate and enhance administrative and clinical tasks such as clinical transcription, medical coding, claims auditing, and patient scheduling. The platform also features AI-powered chatbots for patient interaction, fraud detection, and claims management, providing efficient and accurate support for healthcare providers, administrators, and payers.

Harrison.rad.1 is an AI-based large language model designed to assist radiologists by excelling in critical tasks like detecting findings, localizing issues in images, and providing structured reports. The model scores 51.4/60 in the FRCR 2B radiology exam, and can help with finding detection and localization, structured reporting, and clinical history understanding. It supports multimodal imaging data and emphasizes clinical precision and factual accuracy for enhancing diagnostic processes in healthcare.

Polaris, developed by Hippocratic AI, is a safety-focused large language model (LLM) system designed specifically for real-time, patient-facing healthcare conversations like post-acute care and post-op check-ins. It operates using a constellation architecture, where a primary AI agent handles overall patient interaction, while multiple specialist models focus on specific tasks like medication management, nutrition advice, and lab result interpretation, enhancing consistency, accuracy, and safety.

Insights AI offers healthcare-focused AI solutions, including natural language processing (NLP) tools such as Omniview, a large language model trained on extensive patient data. It supports functions like named entity recognition (NER), optical character recognition (OCR), and de-identification of sensitive data to ensure compliance with healthcare regulations like HIPAA. Its use cases include enhancing clinical documentation, automating data extraction, and supporting clinical decision-making.

John Snow Labs produces AI and NLP models for healthcare, with use cases like healthcare-specific question answering, data de-identification, adverse drug event detection, patient risk adjustment, and clinical decision support.

LLaMA 3, Meta’s latest large language model, offers significant advancements in healthcare applications. It can support a wide range of clinical tasks such as automating medical documentation, improving patient interactions, and aiding in diagnostics through natural language processing. LLaMA 3 has been integrated into tools for real-time surgical assistance, clinical trial automation, and precision medicine.

Med-Gemini is an AI model family from Google, optimized for healthcare tasks like medical imaging, pathology, and genomics. It builds on Google’s Gemini models, using multimodal data and long-context reasoning to improve clinical workflows, such as generating radiology reports and predicting health outcomes from genomic data. Med-Gemini has achieved 91.1% accuracy in USMLE-style exams, and shows potential for assisting healthcare professionals in diagnostic and analytical tasks.

OpenAI offers a variety of models, including GPT-4 and GPT-3.5, accessible through its API platform for tasks like text generation, translation, code assistance, and more. These models are used by developers and enterprises to build AI-driven applications, enabling functions such as conversational AI, content creation, and natural language understanding. OpenAI also provides customization options, allowing businesses to fine-tune models for specific use cases.