Doctors have a new kind of intern: artificial intelligence (AI) point-of-care testing (POCT) tools. And healthcare professionals are welcoming their new digital assistants. 

According to an American Medical Association survey¹ of more than 1,000 physicians, split nearly equally between “tech adopters” and “tech averse” doctors, 72% recognized AI’s benefit in diagnostic settings, 69% believed it would improve work efficiency, and 61% saw improved clinical outcomes.

Not only can AI help reduce physician burnout, but it also shows potential for more accurate diagnoses and, as a result, boosts recovery rates. GlobalRPH reports² that AI detects colon cancer with 98% accuracy, surpassing the 96.9% achieved by trained pathologists. Today, AI algorithms power glucose monitors, rapid strep tests, infectious disease tests, wearable sensors, thermometers, stethoscopes, portable ultrasound devices, and handheld ECGs.

The Global AI/POCT Healthcare Market

The global AI healthcare market surpassed $26.5 billion in 2024 and is projected to reach $187.69 billion by 2030.³

One of the fastest-growing areas where AI is making a dramatic impact is POCT, which is expected to total $91.47 billion by 2032, nearly doubling from $47 billion in 2023.⁴ From AI-enhanced biosensors to real-time imaging interpretation, AI delivers results that once took days. Benefits include higher diagnostic accuracy, shorter hospital stays, fewer redundant tests, and greater access to care in underserved communities worldwide.

The Promise of AI in Point-of-Care Diagnostics

These AI-powered tools enhance data interpretation, pattern recognition, predictive analytics, and real-time decision-making. This capability is especially critical in emergency and critical care, where seconds can mean the difference between life and death.

AI-driven POCT devices offer instant diagnoses, helping providers act quickly and confidently. The result: faster clinical decisions, improved emergency room efficiency, fewer hospital readmissions, and better chronic disease management.

In addition to AI-powered ECGs, your primary care doctor can now use AI-diabetes monitors that analyze blood sugar levels immediately without needing painful finger pricks, dermatology apps that offer quick skin cancer screening, and breath tests for asthma and COPD.

The speed and accuracy of AI POCT are apparent in the emergency room, where hospital staff use AI-assisted portable ultrasounds and chest X-rays, alongside rapid troponin tests to help detect heart attacks. With predictive triage tools, the ER staff can quickly prioritize high-risk patients.

Technologies Behind AI POCT

While AI is the umbrella technology that powers point-of-care-testing devices, machine learning (ML) and deep learning (DL) provide pattern recognition, medical image interpretation, and predictive analytics, which are essential to identifying early signs of diseases before outward symptoms appear. 

ML and DL models train on thousands of clinical cases and medical images, helping them detect subtle signs of diseases that healthcare professionals often miss. Improved sensitivity and specificity with biological samples and predictive analytics capabilities enable the development of personalized treatment plans.

Computer vision allows these tools to remotely interpret X-rays, medical images, blood samples, skin conditions, and wounds. POCT tools also use miniaturized biosensors, which are the key to many of these devices. Bluetooth, IoT, and connectivity along with secure cloud platforms support remote and private diagnoses.

How AI Is Revolutionizing Point-of-Care

Even in today’s advanced healthcare landscape, AI takes diagnostics to a new level. It addresses longstanding challenges like operational inefficiencies, limited access, quality issues, and data fragmentation.

Delays in test results? Not anymore. Staff shortages? AI helps fill the gaps. Inconsistent diagnoses? AI supports standardized, reliable interpretation. 

AI-POCT devices improve diagnostic speed and accuracy across the healthcare landscape—from routine checkups to crowded emergency rooms. These technologies also expand access to rural and underserved areas where labs or specialists are unavailable.

AI also improves consistency in care. Reducing variability in diagnoses between providers helps minimize the risk of misdiagnosis and improves outcomes.

From Fragmented to Fully Connected

One of AI’s most underrated contributions to POCT diagnostics is solving the data fragmentation issue. Traditional POCT devices often operate in isolation and cannot sync with legacy electronic health records (EHRs), which means critical diagnostic insights are often unavailable.

In contrast, AI-powered tools are EHR-integrated, including the latest voice-assisted systems. They automatically structure and upload results into a patient’s health record in real time. This ensures that diagnostic data becomes part of a health record—a complete, patient-centered medical history, such as changes in kidney function over time, which supports better-informed treatment plans and continuity of care.

The effectiveness of AI-powered POCT diagnoses depends on the quality and completeness of the data on which they are trained. With AI-powered tools connected to EHR platforms, diagnoses also have complete patient records, helping them deliver more accurate diagnoses. These EHRs are now more inclusive because AI-powered ambient clinical documentation captures patient-doctor conversations, adding to the information on which to base a diagnosis. 

On top of ambient documentation, Social Determinants of Health (SDOH)—non-medical factors—enrich patients’ records. SDOH include economic status, healthcare access, and environmental issues. For example, homeless patients face greater health challenges to maintaining their treatment plan than someone who has their own home and a job. 

Real-World Cases and Benefits 

AI-POCT is making its impact in a variety of healthcare areas, including:

• Rapid cardiac troponin testing with AI-backed machine learning interpretation, according to a 2024 study,⁵ can rule out heart attacks in eight minutes compared to about 60 minutes for a lab-based test.
• AI-supported glucose monitoring for diabetic patients.
• AI-powered tests are used in mobile clinics during outbreaks like TB or COVID-19.
• Remote diagnostics for virtual-first care using at-home or mobile diagnostic tools, such as blood pressure cuffs, AI-powered ECG, or biosensors, which are shipped to them or provided by area hospitals with remote patient monitoring programs.
• AI stethoscopes and infrared spectral analysis devices detect murmurs, arrhythmias, respiratory conditions, and early-stage brain or lung cancers.

All these POCT tools help reduce diagnostic mistakes, provide faster treatment, and enhance patient outcomes. 

AI’s impact is also felt in rural clinics, far from hospitals, which now have access to portable imaging machines with AI-powered remote interpretation. Virtual nurses also use AI POCT devices, often wearable tech, to monitor vital statistics. Bundled or fixed payments encourage hospitals to adopt POCT to help reduce redundant tests and hospital readmissions.

The Future Outlook: Interoperable, Intelligent, and Everywhere 

As AI-powered point-of-care diagnostics evolve, the future is defined by tools that are interoperable, intelligent, and everywhere. Central to this transformation is federated learning, a technique that allows multiple healthcare institutions to collaboratively train AI models on decentralized data—without ever sharing patient information and violating HIPAA privacy compliance.

Driving this seamless data exchange are interoperability standards like FHIR (Fast Healthcare Interoperability Resources) and Health Level Seven (HL7) that ensure that health data—from lab results to AI-generated diagnostics—are secure and consistently shared across systems, devices, and care settings.

To encourage responsible innovation, regulatory agencies like the FDA have launched programs like the Digital Health Center of Excellence and regulatory sandboxes—controlled environments where developers and providers can safely evaluate novel AI-enabled tools before broad deployment.

Despite the potential of POCT tools, obstacles to adoption exist, such as insurance coverage for the devices, development and testing costs, adequate training for healthcare professionals, scalability, and privacy concerns. These devices are usually designed for one test while a traditional lab can process multiple tests. Can we expect a tricorder like the one Bones used in Star Trek in the future? 

However, current AI-powered point-of-care tests are not science fiction; they already shape how and where care happens. The goal of AI remains the same: empowering frontline healthcare professionals with intelligent tools that meet patients where they are.

References

1. bit.ly/softwaresolutions07251
2. bit.ly/softwaresolutions07252
3. bit.ly/softwaresolutions07253
4. bit.ly/softwaresolutions07254
5. bit.ly/softwaresolutions07255

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Deepak Borole is a project manager at Chetu, a global digital intelligence and software solutions provider, where he oversees general, specialty, and remote healthcare portfolios.