Best 25 AI medical health Tools of 2025

o1 in Medicine is an artificial intelligence model focused on the medical field, aiming to enhance the capacity for processing medical data and improving diagnostic accuracy through advanced language model technology. Developed collaboratively by researchers from UC Santa Cruz, the University of Edinburgh, and the National Institutes of Health, the model has demonstrated its application potential in the medical domain through testing on multiple medical datasets. Key advantages of the o1 model include high accuracy, multi-language support, and a deep understanding of complex medical issues. The development of this model is driven by the current demand in healthcare for efficient and accurate data processing and analysis, particularly in diagnostics and treatment recommendations. Although the research and application of this model are still in preliminary stages, its prospects in medical education and clinical practice are promising.

Diabetica-7B is an optimized large language model specifically for diabetes care. It excels in various diabetes-related tasks including diagnosis, treatment recommendations, medication management, lifestyle suggestions, and patient education. The model is fine-tuned based on open-source models, utilizing specific disease datasets and fine-tuning techniques, providing a reproducible framework that accelerates the development of AI-assisted healthcare. Additionally, it has undergone comprehensive evaluation and clinical trials to verify its effectiveness in clinical applications.

Diabetica-1.5B is a large language model tailored for the field of diabetes care. It excels in tasks related to diabetes such as diagnosis, treatment recommendations, medication management, lifestyle advice, and patient education. The model is developed based on an open-source framework and fine-tuned on specific disease datasets, providing a reproducible framework that accelerates the advancement of AI-assisted healthcare.

Diabetica is an advanced language model developed specifically for diabetes treatment and care. Through deep learning and big data analysis, it provides a wide range of services, including diagnosis, treatment recommendations, medication management, lifestyle advice, and patient education. The models Diabetica-7B and Diabetica-1.5B have demonstrated outstanding performance across various diabetes-related tasks, offering a reproducible framework that allows other medical fields to benefit from such AI technologies.

Health Acoustic Representations (HeAR) is a bioacoustic foundation model developed by the Google Research team, aimed at identifying early signs of disease by analyzing sounds produced by the human body, such as coughs. The model has been trained on 300 million audio samples, utilizing approximately 100 million samples specifically for cough sounds. HeAR can recognize health-related sound patterns, providing a robust foundation for medical audio analysis. The HeAR model outperforms other models across various tasks and demonstrates superior generalization capabilities across different microphones. Additionally, HeAR allows models trained with limited data to achieve high performance, which is crucial in data-scarce medical research fields. HeAR is currently open to researchers to accelerate the development of customized bioacoustic models while reducing the need for data, setup, and computing resources.

MedTrinity-25M is a large-scale multimodal dataset featuring multi-granular medical annotations. Developed by multiple authors, it aims to advance research in medical image and text processing. The dataset's construction involves steps such as data extraction and multi-granular text description generation, supporting various medical image analysis tasks, such as visual question answering (VQA) and pathology image analysis.

The Surgical Robot Transformer is a model that executes surgical operation tasks on the Da Vinci robot using imitation learning. This model overcomes the inaccuracies in forward kinematics of the Da Vinci system through relative motion formulas, enabling the successful training and deployment of policies. One significant advantage of this approach is the ability to leverage a large amount of clinical data containing approximate kinematics for robot learning without additional calibration. The model has demonstrated success in executing three fundamental surgical tasks, including tissue manipulation, needle handling, and suturing.

OculiChatDA is an ophthalmological chatbot large model designed to assist patients with preliminary diagnoses through conversational interaction and offer reasonable medical advice. It possesses image-reading capabilities, allowing it to analyze fundus photographs to identify potential cases of glaucoma or diabetic retinopathy. This model supports multi-turn dialogues across various scenarios, including consultations, inquiries, and casual conversations. It strives to address the issue of uneven medical resource distribution by providing timely and convenient medical services to a wider range of patients.

The UltraMedical project aims to develop specialized general-purpose models for the biomedical field. These models are designed to answer questions related to exams, clinical scenarios, and research questions while maintaining a broad base of general knowledge to effectively handle cross-domain issues. By utilizing advanced alignment techniques, including supervised fine-tuning (SFT), direct preference optimization (DPO), and odds ratio preference optimization (ORPO), training large language models on the UltraMedical dataset creates powerful and versatile models that effectively serve the needs of the biomedical community.

Prov-GigaPath is a whole-slide foundation model for digital pathology research. Trained on real-world data, it aims to support AI researchers in their studies of pathology foundational models and digital pathology slide data encoding. Developed by multiple authors and published in Nature, it is not suitable for clinical care or any clinical decision-making purposes and is restricted to research use only.

Chartnote is a plugin that enables rapid completion of medical documentation. It utilizes generative AI, voice recognition, and intelligent templates to make medical record writing efficient and easy. Its key advantages include improved work efficiency, reduced document writing time, and accurate clinical record provision. Chartnote is suitable for doctors, nurses, and other healthcare professionals.

Developed by HPAI, Aloe is a medical language model optimized based on Meta Llama 3 8B. Through model fusion and advanced prompting strategies, it achieves state-of-the-art performance comparable to its scale. Aloe scores high on ethical and factual metrics, thanks to the combination of red teaming and alignment work. The model provides medical-specific risk assessments to promote the safe and responsible use and deployment of these systems.

Polaris is a highly secure medical-focused large language model (LLM) system developed by Hippocratic AI. Through its constellation architecture and professional support agents, it can perform various complex medical-related tasks. The product is positioned to provide patients with extended, multi-turn voice conversations and offer professional and accurate medical advice. It is priced at $9 per hour. Key features include real-time multi-turn voice conversations, medical information provision and explanation, privacy and compliance checks, medication management and consultation, laboratory and vital sign analysis, nutritional advice, patient history and policy queries, and patient relationship building.

Maintained by the FreedomIntelligence organization, the Apollo project aims to democratize medical AI by providing large language models (LLMs) for the medical domain in multiple languages, covering 600 million people. The project includes models, datasets, benchmarks, and related code.

This application aims to help users record and manage their health information. It allows users to save medical records, set appointment reminders, and medication reminders, making it a great helper for personal health management.

MedRAG is a retrieval-augmented generation (RAG) model specifically designed for the medical field. It combines information retrieval and text generation techniques to provide accurate medical information querying and answering.

CheXagent is a chest X-ray interpretation tool based on visual language foundation models. It leverages clinical large language models to parse radiology reports, visual encoders to represent X-ray images, and designs a network to bridge the visual and language modalities. Additionally, CheXagent introduces CheXbench, a new benchmark designed to systematically evaluate the performance of visual language foundation models on eight clinically relevant chest X-ray interpretation tasks. Through extensive quantitative evaluation and qualitative review by five expert radiologists, CheXagent outperforms previously developed general-purpose and medical domain foundation models on CheXbench tasks.

TongYi Ren Xin is a personal health assistant that provides health report queries, symptom inquiries, drug information, and disease inquiries. All content is AI-generated and intended for medical knowledge popularization only. It does not constitute professional medical advice. Users with health concerns should seek medical attention promptly and follow their doctor's instructions.

AweMyFace is a skincare application that serves as your ultimate guide to flawless skin. It utilizes artificial intelligence to identify acne and displays the percentage of affected areas, helping users understand their individual skin condition. It tracks the impact of user behaviors on acne and provides personalized recommendations and customized reminders to help users improve their skin condition, eliminate acne, and achieve clearer skin. The application supports offline use, and user photos and data are stored only on the device, ensuring they remain private.

Freed's AI medical scribe can help doctors reduce document time and improve work efficiency. It utilizes artificial intelligence technology to automatically recognize a doctor's spoken content and convert it into text records, significantly alleviating the doctor's burden. Freed also boasts a high accuracy rate, capable of accurately understanding and recording the doctor's voice input. The product offers flexible pricing and can be customized according to the needs of the healthcare institution. Freed is positioned as a professional tool aimed at enhancing doctor's work efficiency.

Tali AI helps doctors save time at the point of care by streamlining their administrative tasks. This Chrome extension allows you to directly dictate patient notes into electronic health record (EHR) software. Additionally, the extension enables you to search evidence-based medical databases for information at the point of care without leaving your EHR.

Fibromeditate provides guided meditations and relaxation techniques designed to relieve pain caused by fibroids and reduce stress. Our tools are accessible anytime, anywhere, to help manage fibroid pain and decrease stress levels.

MedGPT is a reliable and convenient app for searching drug and health information. Powered by the innovative ChatGPT API, our app can act as your personal doctor, providing you with comprehensive drug and health information. With an intuitive search function, you can quickly and easily find the information you need, stay informed about your health, and make informed decisions. Whether you're looking for information about a specific drug or want to learn more about a particular health condition, our app can meet your needs. Download our app today and take the first step toward a healthier, happier life!

AWS HealthScribe is a HIPAA-compliant service that leverages the analysis of patient-physician dialogues to help healthcare software vendors build clinical applications capable of automatically generating clinical notes. Features: - Enhance clinical productivity by providing AI-generated notes that are easy to reference, edit, and complete. - Use artificial intelligence responsibly in a clinical setting by providing a traceable textual record reference for each AI-generated note. - Implement a unified, integrated conversational and generative AI service within your application, simplifying implementation. - Use a HIPAA-compliant service to protect patient privacy, suitable for telehealth and clinic consultations. Pricing: Pay-as-you-go pricing, no upfront fees. Use Cases: - Reduce documentation time - Increase medical scribe efficiency - Provide patient-friendly consultation summaries Tags: Clinical Notes, Artificial Intelligence, Healthcare Software