The healthcare industry has a long history of innovation by leveraging new types of tools. Computed tomography, or CT, is a scan that debuted in the 1970s. A digital healthcare platform that spread in the 1980s. And the deployment of medical paper internet devices over the past decade.
Today, AI healthcare tools are the latest product category that companies in this industry can use to increase efficiency and improve the quality of care. Healthcare organizations are increasingly applying a wide range of AI tools tailored to the healthcare industry and more common types of AI services and platforms to address use cases ranging from patient data analysis to patient interaction and drug discovery.
In 2025, we selected some of the key AI tools to drive innovation in the healthcare industry to provide guidance on how AI technology is changing the healthcare industry. It also discusses the challenges and opportunities surrounding machine learning and the use of AI software in the healthcare industry.
AI is nothing new to the healthcare realm. For years, the industry has adopted AI software, using predictive analytics to predict how treatment plans will affect patient outcomes, and applying descriptive analyses to summarise collections of medical records, including other use cases.
However, the debut of production-ready Genai products has opened the door to a new type of artificial intelligence tool in healthcare. While more traditional AI use cases are still important, Genai enables new use cases, such as creating or optimizing molecules as part of the drug discovery process.
Also, note that while some AI tools used by healthcare companies are specifically dedicated to medical use, others are common products such as Genai services aimed at mass consumption by end users. Using the latter can be at high risk, as generic AI tools often do not provide built-in controls to mitigate challenges such as protecting sensitive healthcare data. Still, it is possible to control these risks with the right approach. Moreover, typical products are often cheaper and more accessible than those tailored to the healthcare industry.
Next, let's look at the key AI tools used by hospitals, healthcare management companies, pharmaceutical businesses, insurance companies and other stakeholders in the healthcare industry as of 2025.
The following AI tools are listed alphabetically and are based primarily on Gartner's research into AI use cases and tools in healthcare, particularly on the role of AI in healthcare and life sciences, as well as IDC's perspective on AI and automation in healthcare, and an analysis of genai within healthcare.
1. Add
ADA Health's ADA is an AI chatbot that provides self-service diagnostic services to patients. Ask users about their health and generate personalized ratings. This tool can also direct users to relevant care services. The main focus of ADA Health is to provide health care insights directly to individuals, but doctors and hospitals use it to help patients understand their healthcare options and demand appropriate care.
2. Aidson
Aidson is Merck's AI-assisted drug discovery tool. Its main focus is to identify molecules that can serve as the basis for drugs to assess structure-based strategies that seek to formulate new molecules based on biological targets, using a combination of ligand-based approaches to assess the therapeutic properties of known molecules. Aidson similar products, if successful, promise significant reductions in drug discovery time and costs.
3. Biomorph
Biomorph is another example of creating AI tools essential for drug discovery. It mainly serves as a predictive analytics provider. By analyzing a dataset that explains how compounds affect cells, the software can predict which compounds will achieve the desired effects on cell health. This is a much faster approach than manually reviewing compound data and designing drugs.
4. ChatGpt
Openai's Genai Chatbot, ChatGpt, is a popular AI service best known for its ability to generate text and images in response to open-ended prompts from end users. Given its lack of meeting the needs of healthcare providers, ChatGPT may not seem like an important AI tool for the industry. However, physicians, hospitals and other providers use ChatGPT extensively, especially in clinical settings.
“ChatGpt is attracting attention and has a variety of application scenarios in clinical practice,” wrote academic researchers at Sichuan University in China and Vanderbilt University Medical Center in Tennessee. Some providers use ChatGpt for tasks such as summary clinical notes. Others will indirectly access using services such as Doximity GPT, an AI writing assistant with the same foundation model as chatGPT, with some additional controls to address requirements such as HIPAA compliance mandates.
5. Claude
Like ChatGpt, Claude is a popular AI service that helps users with a variety of tasks. It is primarily subjective to distinguish it from ChatGpt. Advocates often promote Claude as being more expressive and empathetic – a property that helps to explain why some medical clinicians use it to summarise patient interactions and generate content for patient interactions. Claude is also similar to ChatGpt in that clinicians can use it in a “raw” format or take advantage of tools such as HATHR AI, which is based on Claude but has built-in data privacy and compliance capabilities tailored to healthcare use cases.
6. DaxCopilot
Nuance's Dragon Ambient Experience Copilot, or Dax Copilot, is a Genai tool designed to increase the efficiency of clinicians. Among other capabilities, it provides automated documentation of patient visits based on audio conversation capture. Integrated with the widely used Epic, it allows providers to easily generate and submit clinical documents in one step. Under the hood, Dax features a Microsoft Azure Openai service model that reduces data privacy risks and tweaks model performance for healthcare use cases.
7. DoximityGPT
Doximity GPT is essentially the same AI model frontend that powers ChatGPT, but with additional protection to mitigate the challenges of HIPAA compliance. Its main use is to generate clinical documents and communicate with patients. Doximity is becoming a popular product for providers who want the ease of use of ChatGPT with built-in healthcare privacy guard rails.
8. merit
Formerly IBM Watson Health, Merative is an AI-driven analytics platform designed primarily for the healthcare industry. Its use cases focus on analyzing large quantities of clinical and patient data to assist in diagnosis, treatment planning and patient monitoring. So the benefits are examples of healthcare platforms that focus on more general forms of AI, specifically descriptive and predictive analytics. Even in the Genai era, traditional AI tools remain important to the healthcare industry.
9. Moxi
Produced by hardworking robotics, Moxi is a 4-foot-high physical healthcare robot. It expands nursing staff in hospitals and clinics, delivers patient supply and performs tasks such as obtaining lab samples. Moxi is powered by sensors and AI technology, allowing you to intelligently navigate clinical settings. While robots roaming hospital corridors may still feel like part of the future, hardworking robotics says that over 20 healthcare systems use Moxi.
10. Storyline AI
Some telehealth tools and data analytics platforms, Storyline AI, helps healthcare providers connect with patients and develop personalized care plans. The app collects patient data, automatically analyzes it to predict risk, and recommends treatment. Healthcare providers can also talk to patients using live video, chat, email and text.
Chris Tozzi is a freelance writer, research advisor and professor of society. He previously worked as a journalist and Linux system administrator.
