Agentic AI is a new type of AI that can work on many tasks at once. It can use different kinds of data like images, lab results, notes from doctors, and sensors. It makes decisions or suggestions that change as new information comes in. This helps doctors find problems faster, give better treatments, and run hospitals more smoothly.
According to Nalan Karunanayake, who wrote about agentic AI for Elsevier, these systems improve their work step by step using different kinds of data. They aim to give care focused on each patient with fewer mistakes than older AI systems. Agentic AI is used in many areas, from helping in surgeries to scheduling appointments and managing billing.
But because agentic AI works on its own and is complex, it brings challenges that healthcare leaders must watch for.
Hospitals and clinics in the US must follow strict rules like HIPAA to protect patient information. Agentic AI uses a lot of personal health data all the time, which can make it easier for hackers to steal information or for data to be shared wrongly.
A study from Workday shows that because agentic AI makes decisions on its own, it could share data without permission if the right controls are not in place. Also, the way the AI works can be hard to understand, even for experts. This creates big privacy worries. To handle these, healthcare leaders should:
BigID, a company that works with AI rules, says it is also important to train employees about privacy and correct data handling.
Not keeping patient data safe can lead to fines and also cause patients to lose trust, which is very important for good healthcare.
AI bias means the AI treats some patients unfairly. This is a serious problem because it can cause wrong diagnoses or wrong treatment for some groups of people. Agentic AI learns from past data, and if that data is unfair, the AI can become unfair too.
For example, if the AI trains mostly on data from certain races or ages, it might not work well for others. Bias can also grow over time if no one checks the AI’s decisions carefully.
Experts like Edosa Odaro warn about the cost when people do not trust AI and delay decisions or ignore AI advice. Debasmita Das says AI needs regular tests to find and fix bias or mistakes quickly.
To fight bias, healthcare organizations need to:
Without these steps, AI could keep unfair treatment going and lose patient trust.
Governance means rules and processes that keep AI use safe and fair. Good AI governance helps manage problems with ethics, privacy, fairness, and rule-following.
IBM research finds that 80% of business leaders see issues like AI explainability and bias as big barriers to using AI. Healthcare is even more sensitive because it deals with life and personal data.
Governance should include many people such as:
Good governance should have:
The new EU AI Act will affect US organizations working globally by requiring strict AI oversight, especially for healthcare applications. Healthcare groups should keep up with such laws to avoid fines or losing reputation.
It can be hard to say who is responsible when AI makes a mistake because AI acts on its own in many cases.
The EU AI Act says:
In the US, healthcare groups should set ethical rules and clear accountability for AI use. Not doing this can lead to legal and financial problems.
Hans-Jürgen Brueck suggests treating AI like a worker in the company, with rules for performance review and when to stop using it if it does not work well.
Even if AI works by itself, humans must watch its actions and step in if there are problems. Keeping this balance helps doctors trust AI and keeps patients safe.
Agentic AI can help hospitals and clinics by automating everyday tasks. This can reduce errors and save time.
For example, Simbo AI uses AI to handle many phone calls, make appointments, answer patient questions, and send urgent calls to staff without much human help.
This automation can:
Agentic AI can also:
However, these systems must be connected safely to existing health records and management software. Privacy and governance rules must still be followed closely.
Hospital leaders and IT managers need to plan for good system strength, training, and proper management of AI to make sure automation works well and safely.
Using agentic AI means healthcare workers need new skills. This includes not just doctors but also office workers and tech staff.
They need to understand how AI makes choices, watch for mistakes or biases, and follow privacy laws.
Training on AI ethics, security, and data rules is important. Workday found that although nearly all CEOs see benefits in AI, only about half of workers feel positive about it. This shows a trust gap.
To close this gap, organizations should share clear information and involve staff in how AI is governed.
Training should:
Hospitals that do not prepare workers risk using AI poorly, causing safety problems, and having staff resist the technology.
The US has many rules about health data and how technology can be used.
Besides HIPAA, other important regulations are:
Healthcare providers using agentic AI must follow all these laws carefully. Breaking them can mean big fines or other penalties.
For healthcare leaders and IT teams planning to use agentic AI, careful steps are needed:
Overall, using agentic AI in US healthcare can help make care better and make hospitals run smoother. But it also brings challenges with privacy, fairness, and responsibility that must be carefully managed. With good rules and careful steps, healthcare organizations can use AI safely and fairly to serve patients well.
Agentic AI refers to autonomous, adaptable, and scalable AI systems capable of probabilistic reasoning. Unlike traditional AI, which is often task-specific and limited by data biases, agentic AI can iteratively refine outputs by integrating diverse multimodal data sources to provide context-aware, patient-centric care.
Agentic AI improves diagnostics, clinical decision support, treatment planning, patient monitoring, administrative operations, drug discovery, and robotic-assisted surgery, thereby enhancing patient outcomes and optimizing clinical workflows.
Multimodal AI enables the integration of diverse data types (e.g., imaging, clinical notes, lab results) to generate precise, contextually relevant insights. This iterative refinement leads to more personalized and accurate healthcare delivery.
Key challenges include ethical concerns, data privacy, and regulatory issues. These require robust governance frameworks and interdisciplinary collaboration to ensure responsible and compliant integration.
Agentic AI can expand access to scalable, context-aware care, mitigate disparities, and enhance healthcare delivery efficiency in underserved regions by leveraging advanced decision support and remote monitoring capabilities.
By integrating multiple data sources and applying probabilistic reasoning, agentic AI delivers personalized treatment plans that evolve iteratively with patient data, improving accuracy and reducing errors.
Agentic AI assists clinicians by providing adaptive, context-aware recommendations based on comprehensive data analysis, facilitating more informed, timely, and precise medical decisions.
Ethical governance mitigates risks related to bias, data misuse, and patient privacy breaches, ensuring AI systems are safe, equitable, and aligned with healthcare standards.
Agentic AI can enable scalable, data-driven interventions that address population health disparities and promote personalized medicine beyond clinical settings, improving outcomes on a global scale.
Realizing agentic AI’s full potential necessitates sustained research, innovation, cross-disciplinary partnerships, and the development of frameworks ensuring ethical, privacy, and regulatory compliance in healthcare integration.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
