In 2025, Navina, a leader in AI-driven clinical intelligence, secured a $55 million Series C investment led by Goldman Sachs. Their AI technology integrates with electronic health record systems to provide clinical insights, flag potential diagnoses, identify risk factors, and spot medication conflicts.

Navina's technology integrates seamlessly into clinical workflows, earning the trust of over 10,000 healthcare professionals across 1,300 clinics. By transforming fragmented patient data into concise, actionable profiles, Navina empowers clinicians to make informed decisions, reduce missed diagnoses, and alleviate administrative burdens.

This development exemplifies how AI employees are revolutionizing patient support and care, making healthcare more personalized and efficient.

AI Employees for On-Demand Diagnostic Assistance

AI employees assist healthcare professionals by providing immediate diagnostic support using patient data, such as symptoms, medical history, and vital signs. These agentic AI healthcare systems rely on advanced machine learning models trained on vast datasets to offer accurate diagnostic suggestions that aid physicians in real time.

How It Works:

• Data Collection: The AI system gathers data from patient medical records, including symptoms, demographics, and previous health issues.
• Machine Learning Models: AI models, trained on millions of diagnostic cases, are able to suggest possible diagnoses and treatment options based on input data.
• Real-Time Decision Making: Once the data is processed, AI algorithms analyze it in real-time, providing healthcare professionals with diagnostic options and treatment suggestions within seconds.
• Continual Learning: Over time, AI systems refine their diagnostic capabilities by learning from new cases, ensuring that suggestions become more accurate as more data is processed.

​Recently, in 2025, Akido Labs introduced ScopeAI, an AI-enabled medical diagnosis and treatment tool, to New York's ride-share drivers. Collaborating with the Independent Drivers Guild and Workers Benefit Fund, Akido provided on-the-spot healthcare access to drivers who often avoid medical visits due to concerns about unpaid time off.

According to Akido Labs, ScopeAI has reduced diagnostic time by 30% and helped physicians reach more accurate diagnoses, leading to improved treatment and faster recovery times.

Now that AI is improving diagnostic speed let’s take a look at how it's transforming clinical trial matching. This is where AI helps streamline patient recruitment, reducing delays and speeding up the research process.

AI-Driven Clinical Trial Matching: Finding the Right Patient, Fast

Finding the right participants for clinical trials is a labor-intensive process that AI is helping to streamline. AI-driven systems can scan large patient databases, cross-referencing medical histories, genetics, and other factors to identify suitable candidates for trials, significantly speeding up the recruitment process.

How It Works:

• Patient Data Aggregation: AI systems collect and process patient data from various sources, such as electronic health records (EHRs), medical imaging, and genetic profiles.
• Eligibility Criteria Matching: Algorithms match patients to clinical trials based on eligibility criteria like disease type, treatment history, and biomarkers. The AI continuously updates the database to refine its match-making accuracy.
• Automation of Screening Process: AI performs an automated screening of patients, identifying the most suitable candidates without the need for manual intervention by clinical researchers.
• Real-Time Analytics: The AI system provides real-time suggestions and updates on trial availability, reducing wait times for patients and enhancing overall trial efficiency.

Grove AI helps researchers quickly identify eligible patients for clinical trials by analyzing patient health data and demographics. Grove AI maintained a 97% patient satisfaction rate across 165K+ calls, ensuring both efficiency and compliance in clinical trial recruitment.

As clinical trials become more efficient with AI, it’s clear that remote patient monitoring is another area where AI is making a significant impact. By providing continuous care through wearable devices, AI is reshaping how we manage chronic conditions.

Remote Patient Monitoring: Keeping Patients on Track

Remote patient monitoring (RPM) involves using AI-powered wearable devices and mobile apps to continuously monitor patient health data. These devices collect real-time data on vitals, such as heart rate, blood pressure, and glucose levels, sending the information to healthcare providers for analysis and timely intervention.

How It Works:

• Data Collection: Wearable devices collect continuous patient health data, such as heart rate, blood pressure, oxygen levels, and glucose levels. Sensors embedded in the device track vital signs in real-time.
• AI Analysis: AI systems process this data through predictive algorithms to detect irregularities or health risks. The system is capable of identifying early warning signs of conditions like heart disease or diabetes.
• Real-Time Alerts: If abnormal readings are detected, the AI system sends alerts to healthcare providers for immediate intervention. The data is also made available to patients for self-monitoring.
• Patient Engagement: AI systems provide patients with personalized feedback, helping them manage their health proactively, with reminders for medication, exercise, and lifestyle changes.

AliveCor's devices, such as KardiaMobile and KardiaMobile 6L, enable users to record medical-grade ECGs at home. The AI algorithms analyze these ECGs to detect various cardiac conditions, including atrial fibrillation and heart attacks.

Clinical studies have demonstrated that AliveCor's devices can accurately detect arrhythmias, with the Kardia 12L ECG System performing equivalently to standard 12-lead ECGs.

In addition to remote monitoring, AI is also enabling more personalized health management. By tailoring treatment plans to each individual, AI is not only improving outcomes but also ensuring patients are more actively involved in their own care.

Personalized Health Management: Tailoring Care to Each Patient

AI employees help manage chronic conditions by providing personalized treatment plans and care reminders based on individual patient data. These systems integrate medical history, current conditions, and real-time monitoring data to offer recommendations tailored to the patient’s specific needs.

How It Works:

• Patient Profiling: AI analyzes a patient’s medical history, genetic information, and lifestyle data to build a personalized health profile.
• Customized Health Plans: Based on the data, AI systems develop customized health management plans, including medication schedules, dietary recommendations, and exercise regimens.
• Continuous Monitoring and Feedback: AI continuously monitors patient data, adjusting health recommendations based on real-time inputs, such as changes in weight, glucose levels, or blood pressure.
• Proactive Interventions: The system identifies potential health risks early, prompting patients to take preventive measures like altering their diet or modifying their medication.

Omron's IntelliSense™ AFib-equipped blood pressure monitors utilize machine learning algorithms to analyze pressure pulse waves during inflation, enhancing the detection of AFib. A clinical study demonstrated that Omron's AI-powered monitors achieved a sensitivity of 95.1% and a specificity of 98.6% in detecting AFib, underscoring their diagnostic precision.

With cumulative sales surpassing 350 million units worldwide, Omron's blood pressure monitors are among the most recommended by healthcare professionals, reflecting widespread user adoption.

Just as personalized care becomes more accessible through conversational AI, virtual health assistants are enhancing patient engagement. These assistants are available 24/7 to answer questions, schedule appointments, and even provide medication reminders, ensuring patients have continuous support.

Virtual Health Assistants: Revolutionizing Patient Engagement 24/7

AI-powered virtual health assistants are available around the clock to answer patient inquiries, schedule appointments, and manage other healthcare tasks. By leveraging natural language processing (NLP), these assistants communicate effectively with patients, offering a level of accessibility previously unattainable.

How It Works:

• Natural Language Processing (NLP): Virtual assistants use NLP to understand patient queries and respond conversationally, mimicking human interaction.
• Task Automation: AI assistants handle routine tasks such as appointment scheduling, answering health-related questions, and providing reminders for medication.
• Integration with Healthcare Systems: These virtual assistants are integrated into EHRs, allowing them to pull up patient-specific information and provide tailored responses.
• Data Collection for Future Interactions: The AI learns from each interaction, improving its responses and anticipating patient needs over time.

MediBot by Medisafe is an AI chatbot that helps patients manage their medication and appointments. Most patients who used MediBot reported higher satisfaction with their healthcare experience. The platform has helped reduce no-show rates by 40%, improving patient engagement and reducing operational costs for healthcare facilities.

Finally, as AI plays an increasingly pivotal role in managing health, early detection systems, like those used for skin cancer diagnosis, highlight how AI can save lives by providing quicker, more accurate assessments.

AI in Skin Cancer Diagnosis: Catching Problems Early

The Chelsea and Westminster Hospital NHS Foundation Trust has integrated AI into its dermatology services to expedite skin cancer diagnosis. Using an AI application developed by Skin Analytics, staff capture images of suspicious moles using an iPhone equipped with a magnifying lens. The AI analyzes these images within seconds, providing immediate assessments.

Approximately 50% of patients receive an immediate all-clear, while others are scheduled for specialist consultations and treatment. This AI tool boasts a 99.9% accuracy rate in ruling out melanoma and has significantly reduced waiting times, with assessments completed in about five minutes compared to the traditional 20-minute face-to-face examinations.

How It Works:

• Image Analysis: AI systems are trained on extensive datasets of skin lesion images, enabling them to recognize patterns indicative of skin cancer. ​
• Diagnostic Support: Healthcare providers upload high-resolution images of skin lesions to the AI platform. The system analyzes these images for abnormalities such as irregular borders or color variations. ​
• Referral and Monitoring: If malignancy is suspected, the AI system facilitates quick referral to specialists and supports ongoing monitoring of the patient's condition throughout the treatment process.

With all these advances, it’s evident that systems like Ema, which integrate Agentic AI, can make a substantial impact across healthcare operations. Let’s explore how Ema can streamline workflows, improve patient care, and increase efficiency across healthcare systems.

How can Ema help?

Ema, developed by Ema, is an advanced Agentic AI system designed to revolutionize healthcare operations. As a Universal AI Employee, Ema autonomously performs complex tasks, adapts to evolving scenarios, and collaborates seamlessly with other agents, enhancing patient care and operational efficiency.​

Key Features of Ema in Healthcare:

• Generative Workflow Engine™ (GWE™): Ema's core technology automates intricate tasks, reducing the administrative burden on healthcare professionals and allowing them to focus more on patient care.

• EmaFusion™ Model: This proprietary model intelligently combines multiple AI models, ensuring accurate and secure processing of sensitive healthcare data.
• Pre-Built Library of Agents: Ema comes equipped with a diverse set of agents tailored for various healthcare functions, facilitating quick deployment and customization to specific organizational needs.​

Benefits of Implementing Ema:

• Enhanced Patient Care: By automating routine tasks and providing real-time insights, Ema allows healthcare providers to deliver personalized and timely care.​
• Operational Efficiency: Ema streamlines workflows, reduces errors, and optimizes resource allocation, leading to cost savings and improved service delivery.​
• Regulatory Compliance: Ema assists in navigating complex regulatory requirements by automating compliance checks and ensuring adherence to standards.

Conclusion

AI employees for healthcare patient support are making a significant impact on how healthcare is delivered today. From on-demand diagnostics to personalized health management, AI tools are enabling faster diagnoses, more accurate treatments, and better patient engagement. As AI continues to evolve, it promises to make healthcare more accessible and efficient while also improving patient outcomes.

Embracing Agentic AI with Ema signifies a commitment to innovation, efficiency, and superior patient outcomes in the ever-evolving healthcare landscape.​ Learn how Ema's AI Employees can streamline workflows, enhance patient engagement, and support clinical excellence.​ Hire Ema today!