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.

Applied AI Company (AAICO) offers AI models and services in life sciences and healthcare, enabling medical literature analysis, generation of CPT and DRG codes from clinical documentation, and medical record organization and analysis. They build specialty models for these services with training and validation from in-house experts.

Bunkerhill Health provides a platform that helps bring AI algorithms developed by researchers to clinical settings. The company assists with the validation, regulatory approval, licensing, and deployment of these AI solutions across hospitals, particularly focusing on cardiology, oncology, and radiology.

Centaur Labs provides medical data labeling solutions powered by a global network of expert annotators, including medical professionals and students. The platform supports multimodal data, including images, text, audio, and waveforms, to build high-quality datasets for AI development in healthcare and life sciences. Centaur Labs offers scalability and accuracy by using collective intelligence and automated quality checks, making it ideal for medical AI model training and validation.

Cornerstone AI is a healthcare-focused platform that automates the cleaning and preparation of real-world data (RWD) using advanced machine learning techniques. It offers automatic data profiling, error detection, and data standardization. The platform enhances data quality, ensuring it is ready for analysis, crucial for clinical research.

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.

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.

Hoppr offers an API and platform designed to support AI developers in the medical imaging field. It provides a foundational AI model trained on diverse medical imaging data across various modalities, enabling faster and more cost-effective development and fine-tuning of AI/ML applications. The platform includes infrastructure for data management, including de-identification and regulatory compliance, as well as tools for training models in a scalable GPU environment. This helps reduce the time and complexity involved in medical imaging research and application development.

Hume AI provides an API-controlled voice-to-voice AI product that can understand speech and optimize responses for user satisfaction using their proprietary LLM model that combines language modeling and emotional quotient training.

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.

Tuva Health provides services and open source software to transform raw healthcare data into analytics-ready data, simplifying data preparation for healthcare analytics and AI development. Their services include preprocessing, data quality testing, data curation, augmentation, unification, enrichment for AI development, and real-world evidence generation in healthcare.