Top AI Applications Used by Clinicians in the United States 2025

Artificial intelligence has moved from theoretical promise to daily clinical reality. In 2025, AI tools are reshaping how physicians diagnose diseases, document patient encounters, interpret imaging studies, and manage workflows. What was science fiction five years ago is now standard practice in many healthcare settings.

But the AI landscape is vast and confusing. Hundreds of companies claim their AI will revolutionize healthcare. Some deliver on that promise; others don't. For clinicians trying to separate signal from noise, understanding which AI applications actually work, and which ones colleagues are successfully using, matters enormously.

This guide examines the top AI applications clinicians are actually using in 2025, based on adoption rates, clinical utility, and real-world impact. We'll explore how these tools work, which specialties benefit most, and how to implement them effectively in your practice.

The State of Clinical AI in 2025

According to recent data from the American Medical Association, in early 2026, the number of physicians identifying as 'non-users' has plummeted to just 33%. This explosive growth reflects both technological maturation and practical necessity, physicians are drowning in administrative burden, and AI offers genuine relief.

The shift has been dramatic. Healthcare IT News reports that AI implementation in healthcare increased 340% between 2020 and 2024, with the fastest growth in ambient clinical documentation, radiology AI, and diagnostic support tools.

But adoption isn't uniform. Large academic medical centers and integrated health systems lead implementation, while small independent practices lag due to cost and technical barriers. Specialty matters too, radiologists and pathologists have embraced AI faster than primary care physicians, though that gap is rapidly closing.

2025 PHYSICIANS AI REPORT

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Category 1: Ambient Clinical Documentation

The single most widely adopted AI application in clinical medicine is ambient documentation, AI systems that listen to patient encounters and automatically generate clinical notes.

Why Ambient Documentation Dominates

Physicians spend 1-2 hours on documentation for every hour of direct patient care. This administrative burden drives burnout, reduces face-time with patients, and cuts into personal life as doctors finish notes at home in the evening.

Ambient AI documentation promises to solve this: the system listens to your natural conversation with patients, extracts relevant clinical information, and generates a structured note. The physician reviews and approves the note, but the heavy lifting of documentation shifts to AI.

Top Ambient Documentation Tools

1. Nuance DAX Copilot

Microsoft's Nuance DAX Copilot leads the ambient documentation market with the largest install base and most mature technology.

How it works:

• Runs on smartphone, tablet, or computer during patient visits
• Captures natural conversation between physician and patient
• Uses speech recognition and natural language processing to extract clinical details
• Generates structured note formatted for your EHR
• Physician reviews, edits, and approves
• Integrates directly with Epic, Cerner, and other major EHRs

Adoption and results: According to MGMA research, practices using DAX report:

• 70% reduction in documentation time
• 2-3 additional patients seen daily per physician
• Significant improvements in physician satisfaction and burnout scores
• Higher patient satisfaction as physicians maintain eye contact instead of typing

Specialty applications:

• Primary care: Excellent for documenting routine visits, preventive care, chronic disease management
• Emergency medicine: Captures fast-paced encounters with multiple patients
• Orthopedics and surgical specialties: Documents pre-op and post-op visits efficiently
• Psychiatry: Captures detailed mental health encounters while maintaining therapeutic alliance

Cost: Typically $369-$600 per physician monthly, depending on enterprise volume and EHR integration levels depending on volume and contract terms

Dr. Sarah Chen, family physician in Sacramento: "DAX changed my practice. I'm home by 6pm instead of charting until 9pm. My patients comment on how much more engaged I am during visits. It's the best investment I've made in my career."

2. Suki AI Assistant

Suki offers similar ambient documentation functionality with strong adoption in smaller practices and specialty groups.

Key differentiators:

• Voice-command driven with natural language interface
• Strong specialty customization (adapts to specialty-specific terminology and templates)
• Works across multiple EHRs without deep integration
• Lower cost point than some competitors

Adoption: Over 50,000 clinicians across 50+ specialties now use Suki, which recently achieved the deepest bidirectional integrations in the market with Epic, Oracle Health, and Meditech.

Cost: $399-$699 per physician monthly

3. Abridge

Abridge takes a slightly different approach, partnering with major health systems for enterprise-wide deployment.

Unique features:

• Generates patient-friendly summaries alongside clinical notes
• Creates care plan documentation
• Captures specialized encounters (procedure notes, hospital rounds, telehealth visits)
• Strong enterprise security and HIPAA compliance

Many academic medical centers have standardized on Abridge for institution-wide ambient documentation.

4. Nabla Copilot

Nabla serves the international market and U.S. practices seeking flexible, affordable ambient documentation.

Strengths:

• Works in multiple languages
• Simple implementation without complex EHR integration
• AI-generated patient summaries and care plan documents
• Affordable pricing for small practices

Implementation Tips for Ambient Documentation

Start with high-volume clinics: Test ambient AI in settings where physicians see many patients daily, the efficiency gains are most dramatic and ROI fastest.

Train physicians properly: The first 2-3 weeks require adjustment. Physicians must learn to:

• Speak clearly and structure conversations logically
• Review AI-generated notes carefully (don't rubber-stamp)
• Provide feedback to improve AI accuracy
• Handle patient consent for recording

Address patient concerns proactively: Some patients worry about AI recording visits. Explain that:

• Recording is solely for medical documentation
• All data is HIPAA-compliant and secured
• They can decline without affecting care
• The physician remains in full control

Measure outcomes: Track documentation time, after-hours charting, patient volume, and physician satisfaction before and after implementation. Data justifies continued investment and expansion.

Category 2: Radiology AI

Radiology was the first specialty to broadly adopt AI, and radiologists now routinely use AI to detect abnormalities, prioritize worklists, and improve diagnostic accuracy.

Top Radiology AI Applications

1. Aidoc

Aidoc leads in acute care radiology AI, focusing on time-sensitive findings that require immediate attention.

Primary applications:

• Intracranial hemorrhage detection on head CT
• Pulmonary embolism detection on chest CT angiography
• Cervical spine fracture detection
• Large vessel occlusion (stroke) detection
• Pneumothorax detection on chest X-rays

How it helps: Aidoc flags urgent findings and notifies radiologists immediately, accelerating time-sensitive diagnoses. Instead of reading studies in order received, radiologists tackle critical cases first.

A study in Radiology found Aidoc reduced time to stroke diagnosis by 35% and time to treatment by 22 minutes, potentially saving brain tissue and improving outcomes.

Adoption: Over 1,600 medical centers worldwide use Aidoc. In January 2026, Aidoc secured FDA clearance for the industry's first comprehensive foundation model AI (aiOS™), allowing it to analyze over 100 million patient cases annually.

2. Nanox.AI (formerly Zebra Medical Vision)

Zebra Medical Vision offers comprehensive AI analysis across multiple imaging modalities and clinical conditions.

Capabilities:

• Osteoporosis screening on routine CT scans (opportunistic screening)
• Coronary artery calcium scoring
• Fatty liver disease detection
• Vertebral compression fracture detection
• Incidental pulmonary nodule detection

Unique value: Zebra identifies clinically significant incidental findings that might otherwise be missed, enabling preventive interventions.

3. Viz.ai

Viz.ai specializes in stroke and vascular emergency AI, directly notifying stroke teams when large vessel occlusions are detected.

Workflow:

• AI analyzes CT angiography for large vessel occlusion
• Automatically alerts stroke neurologist via smartphone
• Coordinates care team activation
• Tracks time metrics for quality improvement

Clinical trials demonstrate Viz.ai reduces door-to-treatment time by 20+ minutes in acute stroke, significantly improving outcomes.

4. Lunit INSIGHT

Lunit INSIGHT focuses on chest X-ray analysis, detecting abnormalities including:

• Lung nodules and masses
• Pneumonia
• Pneumothorax
• Pleural effusion
• Cardiomegaly

Evidence base: Multiple peer-reviewed studies show Lunit matches or exceeds radiologist accuracy for detecting thoracic abnormalities on chest X-rays.

Radiology AI Implementation Considerations

Integration with PACS and workflows: The best radiology AI integrates seamlessly into radiologist workflows through Picture Archiving and Communication Systems (PACS). Radiologists shouldn't need to access separate systems.

Validation before clinical use: All radiology AI should undergo validation at your institution to confirm accuracy on your patient population and imaging equipment before clinical deployment.

Radiologist final interpretation: AI assists but doesn't replace radiologist judgment. Every AI-flagged study requires radiologist review and interpretation.

Dr. Michael Torres, radiologist at a 500-bed hospital: "Aidoc has fundamentally changed how we prioritize acute studies. We're catching critical findings faster, and I have data showing our door-to-treatment times for stroke and PE have improved significantly."

Category 3: Clinical Decision Support and Diagnosis

AI-powered diagnostic support tools help clinicians synthesize complex clinical data, suggest differential diagnoses, and recommend appropriate next steps.

Leading Clinical Decision Support Tools

1. UpToDate AI-Powered Clinical Decision Support

UpToDate, the evidence-based clinical resource used by millions of clinicians, has integrated AI-powered features that:

• Analyze patient data and suggest relevant UpToDate topics
• Generate differential diagnoses based on clinical presentation
• Recommend evidence-based treatment approaches
• Flag potential drug interactions and contraindications

Why it works: UpToDate's AI builds on the most trusted clinical reference resource in medicine, combining evidence-based medicine with AI pattern recognition.

2. OpenEvidence: Open Evidence Clinical Decision Support

OpenEvidence is an AI-powered clinical decision support platform designed to provide rapid answers to clinical questions grounded in peer-reviewed medical literature. Unlike proprietary knowledge bases, it emphasizes transparent sourcing and direct citation of published evidence.

Functionality:

• Clinicians enter clinical questions in natural language
• AI synthesizes answers from peer-reviewed studies and medical literature
• Provides linked citations to supporting research (e.g., journal articles, guidelines)
• Summarizes evidence for diagnosis, treatment decisions, and clinical management
• Enables rapid literature review at the point of care

Evidence transparency:Responses include source citations, allowing clinicians to verify recommendations and review the underlying research directly.

Value proposition:OpenEvidence enhances evidence-based practice by making medical research instantly accessible and transparent, reducing time spent searching literature while supporting informed clinical decision-making.

Why it works:By combining large language models with curated biomedical literature and transparent citations, the platform supports trustworthy AI-assisted clinical reasoning.

2. Isabel Healthcare

Isabel Healthcare provides AI-powered differential diagnosis support, particularly valuable for complex or unusual presentations.

Functionality:

• Clinician enters patient symptoms, findings, and test results
• AI generates differential diagnosis list ranked by likelihood
• Includes rare and atypical diagnoses that might be overlooked
• Links to evidence and management guidelines

Evidence: Studies show Isabel helps clinicians identify correct diagnoses in 90%+ of cases and reduces diagnostic errors.

Adoption: Used in over 4,000 facilities worldwide

3. Epic's Sepsis Prediction Model

For healthcare systems using Epic EHR, the integrated sepsis prediction model continuously analyzes patient data to predict sepsis risk hours before clinical deterioration.

How it works:

• Analyzes vital signs, labs, and clinical data in real-time
• Calculates sepsis risk score updated continuously
• Alerts care team when risk exceeds thresholds
• Facilitates early intervention with fluids, antibiotics, and monitoring

Impact: Hospitals using Epic's sepsis model have historically reported mortality reductions; however, recent studies show performance varies significantly by hospital type, performing most reliably in lower-acuity community hospitals; however, external validation at centers like Michigan Medicine shows sensitivity can drop as low as 33% in oncology and complex multi-comorbidity populations where sepsis is harder to distinguish from other pathology.

4. Tempus One

Tempus applies AI to oncology, helping oncologists:

• Match cancer patients to appropriate clinical trials
• Predict treatment response based on genomic data
• Identify personalized treatment options
• Analyze treatment outcomes across similar patients

Value proposition: Tempus synthesizes vast amounts of genomic, clinical, and outcomes data that would be impossible for clinicians to analyze manually.

Category 4: Pathology AI

Digital pathology combined with AI is transforming how pathologists analyze tissue samples, improving accuracy and efficiency.

Top Pathology AI Tools

1. PathAI

PathAI offers AI-powered pathology image analysis across multiple tissue types and diseases.

Applications:

• Breast cancer detection and classification
• Prostate cancer Gleason grading
• Gastrointestinal pathology assessment
• Tumor immunology analysis

Clinical validation: PathAI has FDA clearance for several applications and publishes extensively on diagnostic accuracy.

2. Paige.AI

Paige.AI focuses on cancer detection and classification in digital pathology.

Key offerings:

• FullFocus: Whole slide image analysis for cancer detection
• PreciseDx: Detailed tumor characterization
• Integration with major digital pathology platforms

FDA authorization: Paige became the first AI pathology platform to receive FDA authorization (specifically for prostate cancer detection in 2021).

3. Proscia

Proscia provides end-to-end digital pathology workflow with integrated AI.

Comprehensive solution:

• Digital pathology infrastructure
• AI-powered image analysis
• Workflow optimization
• Quality assurance tools

Many academic pathology departments have implemented Proscia as their digital pathology platform with embedded AI capabilities.

Pathology AI Implementation

Digital pathology infrastructure required: AI pathology tools require whole-slide imaging infrastructure to scan glass slides into high-resolution digital images. This represents significant upfront investment.

Pathologist training: Pathologists must learn to work with digital pathology and interpret AI-assisted findings. Training programs typically run 1-3 months.

Regulatory compliance: Ensure AI pathology tools have appropriate FDA clearances for your clinical applications.

Dr. Jennifer Kim, academic pathologist: "AI has become my second set of eyes on complex cases. It flags areas I should look at carefully and helps standardize my grading. I'm more confident in my diagnoses, and my throughput has increased by 20%."

Category 5: Predictive Analytics and Population Health

AI analyzes population-level data to predict which patients are at highest risk for adverse outcomes, enabling proactive intervention.

Leading Population Health AI Tools

1. Health Catalyst COACH

Health Catalyst's COACH platform uses machine learning to:

• Predict hospital readmission risk
• Identify patients at risk for sepsis, falls, or deterioration
• Optimize care management resources
• Guide population health interventions

2. Jvion's CORE

Jvion provides clinical and financial AI that predicts:

• Which patients will likely become high-cost
• Risk for specific complications or readmissions
• Optimal care pathways to improve outcomes
• Financial impact of different care decisions

3. Epic's Cognitive Computing Platform

Epic integrates multiple predictive models into workflows:

• No-show prediction (optimizing scheduling)
• Deterioration index (predicting clinical decline)
• Sepsis prediction
• Readmission risk stratification

Implementation across health systems: Major health systems report 10-25% reduction in avoidable readmissions using predictive AI to target high-risk patients for enhanced care management.

Category 6: AI Scribes and Workflow Optimization

Beyond ambient documentation, several AI tools optimize clinical workflows in specific ways.

Notable Workflow AI Tools

1. Notable Health

Notable automates pre-visit, during-visit, and post-visit workflows:

• Automated patient intake and registration
• Insurance verification
• Pre-visit chart review and preparation
• Post-visit patient communications
• Referral coordination

Healthcare organizations using Notable report 30-40% reduction in administrative staff time spent on routine tasks.

2. Qventus

Qventus applies AI to operational workflows:

• Operating room scheduling optimization
• Perioperative workflow coordination
• Emergency department throughput improvement
• Inpatient bed management

Hospitals using Qventus report increased surgical volumes (10-15%) without adding OR capacity through better scheduling and coordination.

3. Augmedix

Augmedix provides remote medical documentation specialists augmented by AI:

• Live scribes listen to patient encounters remotely
• AI assists scribes in generating documentation
• Human-in-the-loop ensures accuracy
• Notes delivered in real-time during or immediately after visits

This hybrid approach combines AI efficiency with human judgment, particularly appealing to physicians who want some human oversight in documentation.

2025 PHYSICIANS AI REPORT

Access the Complete Report

✓Complete quantitative breakdown of what physicians really think about AI

✓Strategic implications for healthcare organizations and AI companies

✓Sentiment analysis of physician attitudes about AI and the future

Download full report