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In 1966, a television character named Doctor Leonard "Bones" McCoy used a small device to scan, interpret, and diagnose the injuries and illnesses of a fictional crew aboard the now-famous Starship Enterprise.
The Tricorder was a science fiction item for decades. However, as artificial intelligence (AI) continues to grow in use in the medical world, such technologies are becoming ever closer, and the understanding and use of AI are becoming increasingly important.
This article comprehensively examines agentic AI in healthcare—its technologies, real-world impact, challenges, and future—unveiling a new era of intelligent, collaborative, and responsive care.
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Although still emerging in public awareness, agentic AI represents a distinct approach to using artificial intelligence to find information and create, problem-solve, make decisions, and take action.
First introduced at the Dartmouth Conference in 1956, AI has drastically changed from conceptual programming to the information powerhouse it has become today.
The concept of artificial intelligence has a rich history that originated in science fiction, going back to W. Grove's 1889 novel The Wreck of a World.
The concept of AI has been so pervasive that Time Magazine published an article in 2025 exploring ways AI is turning Star Trek technology into reality.
The public is most aware of generative AI, the type of AI used in an internet search, Chat GPT, or creating a picture or logo. Agentic AI, however, is intended to find information and make decisions using that information.
For the medical world, agentic AI provides a powerful new tool to help understand, diagnose, and treat diseases.
Agentic AI uses advanced language learning models (LLM) like GPT-4 or LLaMA, includes images, and then applies them to a reasoning framework such as Chain of Thought (CoT) or Tree of Thought (ToT) to help inform diagnostic reasoning, although human oversight remains essential.
Once the data has been collected and interpreted, Agentic AI uses dynamic planning tools, memory retention, and tool-use automation to create an autonomous model that can interact, learn, and progress independently.
The role of agentic AI in healthcare
It should come as no surprise that the diagnostic potential of agentic AI is exciting the medical world. So much so that both MIT and Harvard already offer courses and certifications in using agentic AI in the medical world.
In the aforementioned Time article, it‘s theorized that "AI could significantly contribute to developing a real-life (medical) Tricorder by powering the algorithms used to analyze medical data, integrating sensor technologies such as ultrasound, MRI, and electrocardiogram (ECG)."
For example, in diagnostic imaging, AI algorithms can analyze X-rays, MRIs, ultrasounds, CT scans, and DXAs and assist physicians in identifying and diagnosing diseases by analyzing large amounts of patient data, including:
The programs could then rapidly compare such data to millions of records and research information to create an overall diagnostic picture. M3D-LaMed is an AI model that can compare and learn from more than 120,000 images to help create a rapid diagnosis.
The program Med-Flamingo uses paired and interleaved medical image-text data from publications and textbooks to create an overall understanding of images being analyzed.
Such programs show significant potential in pulmonology, where multiple physical tests, diagnostic images, and blood work are often required to form a comprehensive understanding before a diagnosis is reached.
Currently, AI applications in pulmonology include diagnostic aids for everything from tuberculosis to chronic obstructive pulmonary disease (COPD).
Diagnostic support & medical imaging, where AI agents can accurately identify anomalies in X-rays, MRIs, and CT scans.
Personalized treatment & predictive analytics, where AI agents personalize care pathways by combining genetic, lifestyle, and behavioral data.
Virtual health assistants and patient monitoring offer round-the-clock patient support, answering questions, reminding patients to take medications, and alerting providers of abnormal vital signs.
Automating administrative tasks such as scheduling, insurance processing, and clinical documentation.
Enhancing patient experience where AI agents offer hyper-personalized care journeys reduces waiting times and increases patient satisfaction.
Fraud detection & billing accuracy.
Mental health support, where conversational AI offers non-judgmental, anonymous support for individuals dealing with anxiety, depression, or stress.
Real-world implementations
In a 2024 article published in Nature Oncology, deep learning models are described as advancing cancer research and oncology. However, the article states they still require human engagement and oversight to perform complex multi-step workflows.
Agentic AI, empowered by large language models, promises the ability to plan, execute, and optimize multi-step reasoning in biomedical research.
The Mayo Clinic has partnered with VoiceCare AI to streamline insurance calls, medical authorizations and pre-authorizations, and documentation, freeing up clinical and office workers for more direct patient care and interactions.
New York University has created NYUTron, a large language model platform trained on a decade of clinical notes from NYU Langone Health's records. It can potentially optimize the hospital's workflow, is HIPAA-compliant, and is cloud-based for security.
Researchers use agentic AI programs to help identify potential research participants, monitor participants for adverse events, design protocols, and plan commercial grant cycles, potentially reducing a project by several months of non-research work.
Challenges and ethical considerations
As with any new development in patient care and information, the need for security and ethical use is at the forefront of discussions.
A 2022 study on the ethics of AI in medicine stated, "Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians when they are most vulnerable, it is crucial to remember this."
The same study went on to state that no formal regulations were in place to address these issues.
Indeed, when the concept of carbon footprints and the purchase and sale of carbon credits was introduced, the well-known environmental scientist Julio Friedmann noted that new technologies would bring about entirely new jobs that did not exist just a few short years ago.
As in environmental carbon regulations 10 years ago, there’s now a need for specialists in the legal and ethical use of agentic AI, a field of specialty that did not exist just two years ago.
HIPAA is an area of focused concern, as the protection of personal data and, specifically, medical data, remains a key issue for healthcare throughout the USA.
Oversight of AI systems using models based on oversight of human workers is being developed to ensure data security in this developing time.
Another area of concern is gender and racial equity. A 2025 study suggested that current AI models not only fail to create gender equity but exacerbate the issue due to embedded inequities written into their code.
Frequently asked questions (FAQs)
Consider the following frequently asked questions about agentic AI in healthcare.
What’s the difference between agentic AI and traditional AI in healthcare?
Agentic AI is designed to gather information, analyze it, interpret it, and make decisions based on that information.
Can agentic AI make clinical decisions without human input?
Current models require human interaction before taking any action on behalf of a patient.
How does agentic AI protect patient privacy and data?
Programs currently in use are HIPAA compliant, and more stringent protections are being developed.
What are the limitations of agentic AI in current hospital settings?
Currently, agentic AI is being used as an additional tool, not an autonomous entity.
Which healthcare providers are currently using agentic AI?
Most of the current agentic AI use is in hospitals, especially in data interpretation and insurance authorizations.
Can patients trust diagnoses or care plans generated by autonomous agents?
While agentic AI can assist in generating care plans, these are currently reviewed and approved by licensed healthcare providers. This may change at some point, but there’s still a human factor for now.
How will agentic AI affect medical job roles in the next five years?
There’s a growing field of Healthcare AI, which is developing in all areas of healthcare, from testing and analysis to legal and ethical uses.
Key takeaways
As with most of the world, AI is having an ever-growing effect on the medical field. From analyzing vast amounts of data for test interpretations to ensuring proper protocols are followed for insurance claims and privacy, medical AI is here to stay.
There’s a growing need for specialists who understand agentic AI and can implement and monitor it in the healthcare field.
Security, equity, and privacy are areas of significant concern, and many medical facilities are working to ensure that the growing use of AI does not risk patients' private data.
The use of agentic AI is exciting for patients and medical staff alike. Physicians can interpret tests not only with their own knowledge but also rapidly integrate thousands of test results and studies.
AI has the chance to become a trusted digital colleague for clinicians, a navigator for patients, and a catalyst for equitable, efficient global health.
Ready to start delivering better patient care?
Join 100,000 healthcare providers who rely on Fullscript to dispense top-quality supplements and labs to their patients.
The information in this article is intended for healthcare practitioners for educational purposes only, and is not a substitute for informed medical, legal, or financial advice. Practitioners should rely on their own professional training and judgement, and consult appropriate legal, financial, or clinical experts when necessary.
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