Agentic AI refers to AI designed with human-like autonomy to carry out specific tasks without constant human intervention, so Agnetic AI in healthcare is an autonomous system effectively acting as another medical team member alongside human clinicians.
By operating independently yet collaboratively, agentic AI systems extend the capabilities of healthcare teams, handling routine or data-intensive duties and allowing clinicians to focus on complex patient care, so let me tell you more about Agentic AI in Healthcare, from use cases to best practices.
To implement agentic AI effectively, healthcare organizations should initiate targeted pilot projects, invest in high-quality data integration, maintain human oversight for critical decisions, and prioritize transparency in AI. Partnering with skilled nearshore AI developers can also accelerate development and ensure adherence to compliance standards. By leveraging these strategies, healthcare leaders can harness AI’s full potential to improve patient outcomes and operational efficiency.
Healthcare organizations have begun implementing AI agents primarily in non-clinical domains, such as appointment scheduling and insurance authorization, where these tools can operate with minimal risk.
However, more advanced clinical applications emerge as systems prove their safety and reliability. Below are I explain some real-world use cases and successes of agentic AI in healthcare today:
In medical imaging, for example, Google developed an AI agent for breast cancer screening that autonomously analyzes mammograms with remarkable accuracy. This system catches subtle signs of cancer often overlooked by human radiologists, reducing false-positive readings by 9.4% and false negatives by 5.7% , meaning fewer missed cancers and fewer needless scares.
Such AI doesn’t just assist doctors; it actively learns from each scan and adapts its algorithms, sharpening its diagnostic acumen over time.
Researchers in Australia recently demonstrated an autonomous AI that reads lung ultrasound videos to diagnose pneumonia and COVID-19 with 96.57% accuracy, explaining each result to help doctors trust its decisions.
These examples show how agentic AI can act as a second set of eyes, parsing through complex images or data to fade concerns and suggest probable diagnoses. By augmenting clinical decision support, agentic AI elevates diagnostic precision and confidence, which is critical in fields like oncology, radiology, and pathology.
Agentic AI agents enable more proactive, personalized care in patient monitoring and chronic disease management.
For example, in diabetes management, Livongo’s digital health platform leverages agentic AI to continuously analyze patients’ glucose readings, diet logs, and activity levels. Instead of just displaying data, the AI autonomously generates personalized recommendations and real-time alerts, for instance, warning a patient and suggesting an immediate snack if it detects their blood sugar is trending dangerously low.
In the ICU, autonomous monitoring like Philips’ IntelliVue Guardian system tracks patient data streams and predicts critical events; when risk thresholds are crossed, it alerts the care team immediately. This has led to notable drops in ICU mortality and faster recovery times by ensuring no subtle sign of decline goes unnoticed.
From the hospital ward to the home, these agentic AI applications act as tireless sentinels, watching over patients 24/7, detecting anomalies, and initiating the first response, such as notifying providers or adjusting therapy. The outcome is fewer emergencies and better manage chronic conditions, as issues are addressed before they escalate.
Agentic AI drives healthcare research and development to new speeds, especially in pharmaceutical discovery. Traditional drug discovery is expensive and years-long, but AI agents can massively compress parts of that timeline.
An example comes from Insilico Medicine’s AI platform, which autonomously identifies promising drug targets and designs novel molecular compounds. In 2024, Insilico’s agentic AI system discovered a new drug candidate for pulmonary fibrosis in only 18 months, which usually takes several years.
An AI agent might scan thousands of medical papers and patient records to find patterns or suggest hypotheses, acting as a tireless research assistant generating insights. Pharmaceutical companies are beginning to integrate these agents to decide which drug candidates to pursue, design smarter clinical trials, and even monitor drug safety post-launch.
Agentic AI is set to revolutionize how we discover therapies; by autonomously exploring data, generating hypotheses, and learning from results, AI can uncover breakthroughs much faster than traditional methods, potentially bringing lifesaving drugs to patients years sooner.
Read our blog about the best AI Agents Use cases for Business
Here are a few best practices so you can implement Agnetic AI in your healthcare solution with success:
A practical strategy for hospital executives and product managers is to begin with targeted pilot projects. Identify a high-impact area that is relatively low-risk (for example, automating appointment scheduling or insurance verification) to deploy an AI agent and prove its value. This aligns with expert advice to demonstrate success in a controlled domain and then scale up.
Early wins build internal confidence and help refine the technology under real-world conditions. Once the kinks are worked out and ROI is evident, you can expand agentic AI into more critical workflows.
Healthcare data can be messy, siloed, and governed by strict privacy rules. Investing in robust data integration, for example, using interoperable APIs and data lakes is essential so your AI agents have a 360° view of the information they need.
Moreover, it ensures the data is representative and unbiased; if an AI learns from historical records lacking diversity, it could perpetuate biases in its autonomous decisions. Interdisciplinary clinicians and data scientists teams should work together to curate training data and validate AI outputs for quality and fairness.
No matter how autonomous an AI agent is, healthcare providers should keep a human in the loop for oversight, especially in clinical applications. This doesn’t mean stifling the AI’s autonomy but rather setting up checkpoints or alerts that a clinician can review.
For example, an AI that drafts treatment plans should have a physician approve the final plan, or a diagnostic agent flagging an unusual finding should present an explanation for a radiologist to verify.
Human judgment remains the ultimate backstop for now, and the best agentic AI systems are designed to complement, not replace, human decision-making. Establish clear protocols for when and how humans intervene if an AI’s recommendation seems off. Over time, as trust in the AI grows, these protocols can be adjusted, but keeping clinicians engaged ensures accountability and safety.
How an AI agent arrives at its actions is as important as the actions themselves, especially to win buy-in from healthcare professionals. Physicians and nurses are far more likely to trust and adopt an AI assistant if it can explain its reasoning in understandable terms (e.g., highlighting the patient data points that led it to issue a sepsis alert)
Engineers should incorporate explainable AI techniques, such as attention maps for images or audit trails for decisions so that each recommendation or action by the agent can be traced and understood. This transparency is also crucial for compliance; regulators may soon require documentation on AI decision processes for approval in clinical settings.
In summary, explainability should be treated as a core feature, not an afterthought, when developing agentic AI for healthcare.
Healthcare CIOs and innovation officers should consider partnering with experienced AI developers in LATAM or other nearshore regions to augment their internal teams.
These partners can bring technical skills and practical experience from similar projects. By leveraging a nearshore team, you gain flexibility and can scale the development effort quickly and cost-effectively as the project scope grows without long hiring delays. Today, many successful healthcare AI products are built by distributed teams that combine local clinical insight with nearshore engineering talent.
This model lets you iterate faster and stay competitive with more prominent players. The key is to vet nearshore vendors for strong healthcare domain knowledge, proven security practices, and references from health industry clients to ensure they meet the bar for quality and compliance.
Those who invest early by partnering with skilled nearshore AI developers to jumpstart projects or pilot AI agents in key service lines stand to gain a serious competitive edge.
Empower your Healthcare app with our LATAM AI experts! Book a Free Call
Traditional AI in healthcare typically functions as an analytical tool that provides insights or recommendations but requires human decision-making to take action. Agentic AI goes a step further by acting on insights autonomously.
-Improved accuracy in diagnostics and early disease detection.
-Faster patient care through real-time AI-driven decision-making.
-Reduced workload and burnout for healthcare professionals.
-Lower operational costs by automating administrative and logistical tasks.
-Enhanced patient outcomes through predictive analytics and personalized treatments.
-Regulatory compliance: AI decisions must adhere to strict healthcare regulations (e.g., HIPAA, GDPR).
-Data integration: AI systems must access and process patient data from multiple sources (EHRs, lab reports, etc.).
-Trust and transparency: Physicians and patients need explainability in AI-driven decisions.
-Security risks: AI agents handling sensitive patient data must have robust cybersecurity measures.
-Human oversight: Critical healthcare decisions still require a human-in-the-loop approach to ensure safety.
Have you ever questioned how self-driving cars navigate without human input, how chatbots can carry…
Advanced prompt engineering strategies are important when extracting maximum value from Large Language Models (LLMs).…
Today, I will discuss which one is better, Python vs Node.js for AI development, so…
At this point, if AI isn’t part of your application, you’re falling behind in a…
As a CEO, I know that attending the top AI conferences 2025 is an excellent…
Why is Python frequently regarded as the top programming language for developing Artificial Intelligence? Based…