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10 top AI tools in healthcare for 2025
AI tools are transforming healthcare by enhancing efficiency and care quality through predictive analytics and tailored diagnostics. Explore some of the major tools on the market.
The healthcare industry has a long history of innovation by taking advantage of new types of tools: computed tomography, or CT, scans debuting in the 1970s; digital healthcare platforms that became widespread in the 1980s; and the deployment of internet of medical things devices over the past decade.
Today, AI healthcare tools have become the latest product category that businesses in this industry can use to gain efficiency and improve the quality of care. Healthcare organizations are increasingly applying a wide range of AI tools -- including those tailored to the healthcare industry and more generic types of AI services and platforms -- to address use cases ranging from patient data analysis to patient interaction and drug discovery.
To provide guidance on how AI technology is changing the healthcare industry, we selected some of the major AI tools driving innovation in the healthcare industry in 2025. We also explain the challenges and opportunities surrounding the use of machine learning and AI software in the healthcare industry, such as compliance risks linked to generative AI (GenAI) and AI's unique role in supporting use cases such as drug discovery.
AI is not new to the healthcare realm. For years, the industry has employed AI software, using predictive analytics to anticipate how treatment plans will affect patient outcomes and applying descriptive analytics to summarize collections of healthcare records, among other use cases.
However, the debut of production-ready GenAI products has opened the door to new types of artificial intelligence tools in healthcare. While more traditional AI use cases remain important, GenAI enables novel use cases, such as creating or optimizing molecules as part of the drug discovery process.
Note, too, that while some AI tools used by healthcare companies are purpose-built for healthcare specifically, others are generic offerings -- such as GenAI services intended for mass consumption by end users. Using the latter can be riskier because generic AI tools often don't provide built-in controls to mitigate challenges such as securing sensitive healthcare data. Still, it's possible to control these risks with the right approach. In addition, generic offerings are often less expensive and easier to access than those tailored to the healthcare industry.
Now, let's examine the leading AI tools used by hospitals, healthcare administration companies, pharmaceutical businesses, insurers and other stakeholders in the healthcare industry as of 2025.
The following AI tools are listed alphabetically and are based primarily on research into AI use cases and tools in healthcare from Gartner, especially its report on AI's role in healthcare and the life sciences, and IDC perspectives on AI and automation in healthcare and an analysis of GenAI within healthcare.
1. Ada
Ada, from Ada Health, is an AI chatbot that offers self-service diagnostic services to patients. It asks users questions about their health and then generates a personalized assessment. The tool can also direct users to relevant care services. While Ada Health's primary focus is on providing healthcare insights directly to individuals, doctors and hospitals can use it to help their patients understand healthcare options and request the appropriate care.
2. Aiddison
Aiddison is an AI-assisted drug discovery tool from Merck. Its primary focus is identifying molecules that could serve as the basis for drugs using a combination of ligand-based approaches, which assess the treatment properties of known molecules, and structure-based strategies that attempt to formulate new molecules based on a biological target. Aiddison and similar offerings promise vast reductions in drug discovery time and costs if successful.
3. BioMorph
BioMorph is another example of an AI tool making essential inroads in drug discovery. It primarily functions as a predictive analytics offering. By analyzing data sets that describe how compounds affect cells, the software can predict which compounds will achieve a desired effect on cell health -- an approach that is much faster than manually reviewing compound data and designing a drug.
4. ChatGPT
ChatGPT, the GenAI chatbot from OpenAI, is a generic AI service best known for its ability to generate text and images in response to open-ended prompts from end users. Given that it doesn't cater to the needs of healthcare providers, ChatGPT might not seem like a key AI tool for this industry. However, doctors, hospitals and other providers use ChatGPT extensively, especially in clinical settings.
"ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice," wrote academic researchers from Sichuan University in China and Vanderbilt University Medical Center in Tennessee. Some providers use ChatGPT for tasks such as summarizing clinical notes. Others access it indirectly using services such as Doximity GPT, an AI writing assistant powered by the same underlying models as ChatGPT, with some extra controls to help address requirements such as HIPAA-compliance mandates.
5. Claude
Like ChatGPT, Claude is a generic AI service that assists users with various tasks. What differentiates it from ChatGPT is mainly subjective; proponents often tout Claude as more expressive and empathetic -- characteristics that help explain why some healthcare clinicians use it to summarize patient interactions and generate content for patient interactions. Claude is also similar to ChatGPT in that clinicians can use it in its "raw" form or take advantage of tools, such as Hathr AI, that are based on Claude but have built-in data privacy and compliance capabilities tailored to healthcare use cases.
6. Dax Copilot
Nuance's Dragon Ambient eXperience Copilot, or Dax Copilot, is a GenAI tool designed to increase clinician efficiency. It offers automated documentation of patient visits based on the capture of voice conversations, among other capabilities. It integrates with Epic, a widely used electronic health record platform, making it easy for providers to generate and file clinical documentation in one step. Under the hood, Dax is powered by a Microsoft Azure OpenAI Service model, with enhancements to mitigate data privacy risks and tailor model performance for healthcare use cases.
7. Doximity GPT
Doximity GPT is essentially a front end for the same AI models that power ChatGPT, but with extra protections to mitigate HIPAA compliance challenges. Its main uses are generating clinical documentation and communicating with patients. For providers who want the ease of use of ChatGPT with built-in healthcare privacy guardrails, Doximity is becoming a popular offering.
8. Merative
Merative, formerly IBM Watson Health, is an AI-powered analytics platform designed primarily for the healthcare industry. Its use cases center on analyzing large quantities of clinical and patient data to help with diagnoses, treatment planning and patient monitoring. As such, Merative is an example of a healthcare platform that uses more conventional forms of AI -- specifically, ones that center on descriptive and predictive analytics. It's a reminder that even in the age of GenAI, traditional AI tools remain critical to the healthcare industry.
9. Moxi
Moxi, produced by Diligent Robotics, is a 4-foot-tall physical healthcare robot. It augments nursing staff in hospitals and clinics by performing tasks such as delivering patient supplies and fetching lab samples. Moxi is powered by sensors and AI technology that enable it to navigate clinical settings intelligently. While robots roaming hospital hallways might still feel like a part of the future, Diligent Robotics said more than two dozen healthcare systems use Moxi.
10. Storyline AI
Part telehealth tool and data analytics platform, Storyline AI helps healthcare providers connect to their patients and formulate personalized care plans. The app collects patient data and then automatically analyzes it to predict risks and recommend treatments. It also enables healthcare providers to talk to their patients using live video, chat, email and text.
Chris Tozzi is a freelance writer, research adviser, and professor of IT and society. He has previously worked as a journalist and Linux systems administrator.
