Customizable tools and workflows
Online scheduling
Client management
Telehealth services
Client portal
Automated workflows
Invoicing and payments
Niah AI, Carepatron, iHairium, Generative AI Playbook by PromptxAI, Product Prompt, Midjourney, Contents.com, Components AI, ClinicIQ, PetsApp are the best paid / free generative ai in healthcare tools.
Generative AI in healthcare refers to the application of advanced artificial intelligence techniques, such as deep learning and natural language processing, to generate novel insights, solutions, and personalized treatments in the medical field. This technology has the potential to revolutionize healthcare by enabling more accurate diagnoses, developing new drugs and therapies, and improving patient outcomes through data-driven decision-making.
Core Features
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Price
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How to use
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Carepatron | Customizable tools and workflows | Manage client notes, forms, scheduling, client management, telehealth services, invoicing, and more | |
PetsApp | Text and SMS chat | Text and SMS chat, appointment booking, reminders, digital payments, video calls, AI-assisted communication (CoPilot), Wellness and Loyalty Plans | |
Midjourney | Explore hundreds of AI art styles | To use Midjourney, simply copy the prompt, swap the subject, and drop it into your favorite generator. It's completely free! | |
Contents.com | content ideation | Login to the platform, choose from a variety of tools such as AI Chat, AI Writer, AI Art, Audio ↔ Text, and AI Translation to support your content creation needs. Generate high-performing, original, and SEO-optimized content for your digital channels. | |
Ibex | AI-powered cancer detection | Watch the video to learn how to use Ibex's field-proven, AI-powered diagnostic solutions for better cancer care | |
Components AI | Visually design custom design tools, generative components, pages, and sites |
Free Free Basic features with limited access
| Build custom design tools without writing any code. Create responsive components, pages, and sites that you can use in any type of web project. Style markup templates with curated themes or your own design tokens. |
Medicodio | AI-powered code search function | To use Medicodio, healthcare facilities, RCM companies, and medical coders can access Codio, the software provided by Medicodio. It offers two main solutions: SaaS (Software as a Service) and MCaaS (Medical Coding as a Service). With SaaS, users can access Codio from anywhere to increase coding efficiency and reduce errors. With MCaaS, users can let Medicodio handle the complete medical coding process for them. Users can also integrate Codio with their existing EHR/EMR systems or physician's notes for seamless data entry and analysis. | |
NEON | Real-time conversation and engagement | To use NEON, simply interact with the virtual beings through a digital interface. They can respond to conversations, engage in real-time dialogue, and provide assistance based on the user's needs or queries. | |
iHairium | The core features of iHairium include AI diagnostics for hair and scalp conditions, online counseling from top trichologists, blog tips from physicians, a world ranking of hair transplant clinics, and global coverage in real time. The app is developed in collaboration with trichologists and other hair and scalp experts to provide accurate and fast diagnosis. | iHairium is an online hair and scalp service with AI-diagnostics, providing help with hair loss and baldness. Users can download the iHairium app to achieve their hair and scalp goals, receive personalized recommendations, and access consultations with trichologists, dermatologists, and nutritionists. | |
Claude Prompts | Text generation | To use Claude Prompts, craft specific and clear instructions or context for the model, input them into a chosen platform or tool, then generate and refine text outputs iteratively based on the model's responses. |
Oncology: AI-driven analysis of medical images and genomic data to improve cancer diagnosis, staging, and treatment planning.
Cardiology: Predictive modeling of cardiovascular risk factors and AI-assisted interpretation of ECG and cardiac imaging data.
Neurology: AI-based analysis of neuroimaging data and electronic health records to aid in the diagnosis and management of neurological disorders.
Infectious Diseases: AI-driven surveillance and prediction of disease outbreaks, as well as optimization of antibiotic prescribing practices.
Mental Health: AI-powered chatbots and virtual assistants for mental health screening, monitoring, and therapy delivery.
User reviews of generative AI in healthcare are generally positive, with many healthcare providers and researchers praising its potential to improve patient outcomes and accelerate medical research. However, some users express concerns about the interpretability and transparency of AI models, as well as the need for robust data governance and ethical frameworks. Overall, reviewers agree that generative AI is a powerful tool for healthcare innovation, but one that must be developed and deployed responsibly in collaboration with domain experts and stakeholders.
A patient undergoes a routine medical imaging scan, which is analyzed by an AI system to detect early signs of cancer that may have been missed by human radiologists.
A healthcare provider uses an AI-powered clinical decision support tool to generate personalized treatment recommendations based on a patient's genetic profile, medical history, and lifestyle factors.
A pharmaceutical company leverages generative AI to design novel drug compounds with desired therapeutic properties, accelerating the drug discovery process and reducing development costs.
Implementing generative AI in healthcare typically involves the following steps: 1) Collecting and preprocessing large amounts of patient data, including electronic health records, medical images, and genomic information. 2) Training deep learning models on this data to identify patterns, predict outcomes, and generate novel insights. 3) Validating the AI models through rigorous testing and clinical trials to ensure their safety and efficacy. 4) Integrating the AI systems into existing healthcare workflows and decision-making processes, such as diagnostic tools and treatment planning software. 5) Continuously monitoring and updating the AI models as new data becomes available to improve their performance and adapt to changing healthcare needs.
Improved diagnostic accuracy and early disease detection
Accelerated drug discovery and development timelines
Personalized treatment plans tailored to individual patient needs
Enhanced patient outcomes and quality of life
Reduced healthcare costs through more efficient resource allocation and preventive care