24  AI Tools Every Physician Should Know

TipLearning Objectives

This chapter provides a curated, evidence-based guide to AI tools physicians can use immediately. You will learn to:

  • Identify FDA-cleared and clinically validated AI tools by specialty
  • Understand capabilities and limitations of each tool category
  • Evaluate tools appropriate for your practice setting
  • Navigate privacy, liability, and reimbursement considerations
  • Distinguish evidence-based tools from marketing hype
  • Access hands-on resources for learning AI tools

Practical, actionable guidance for immediate implementation.

The Clinical Context: Hundreds of AI tools market to physicians. Most lack rigorous validation. This chapter cuts through hype to identify evidence-based, FDA-cleared, or widely-adopted tools physicians can actually use safely and effectively.

Tool Categories:

  1. Clinical Decision Support
  2. Diagnostic AI (Imaging, Pathology, Dermatology)
  3. Documentation and Ambient Scribe
  4. Literature Search and Synthesis
  5. Patient Communication
  6. Specialty-Specific Tools

Critical Selection Criteria:

✅ FDA clearance or peer-reviewed validation ✅ Evidence from prospective studies ✅ Real-world deployment at multiple institutions ✅ Clear clinical use case ✅ Reasonable cost and ROI ✅ EHR integration or minimal workflow disruption

Top Tier: Highest Evidence Tools

🥇 IDx-DR (Diabetic Retinopathy Screening) - FDA-cleared, autonomous diagnostic - Prospective RCT validation - CPT reimbursement established - Deployed widely in primary care

🥇 Viz.ai (Stroke, PE Detection) - FDA-cleared multiple indications - Reduces time-to-treatment - Deployed at 1400+ hospitals - Strong evidence base

🥇 Paige Prostate (Pathology AI) - FDA-cleared - Prospective validation - Deployed in clinical pathology labs

🥇 Nuance DAX / Ambient Documentation - High physician satisfaction - 50-70% documentation time reduction - Widely adopted

Strong Evidence Tier:

📊 Aidoc (Multiple Radiology Applications) - ICH, PE, pneumothorax, C-spine fractures - Multiple FDA clearances - Deployed at 1000+ sites

📊 Arterys / Circle CVI (Cardiac MRI Quantification) - FDA-cleared - Improves measurement standardization - Integrated into scanner workflows

📊 Lunit INSIGHT (Chest X-Ray, Mammography) - FDA-cleared - Strong performance in trials - International deployment

Emerging But Promising:

⚠️ AI Scribes (Suki, Abridge, DeepScribe) - High user satisfaction - Limited long-term outcome data - Privacy considerations

⚠️ UpToDate AI Features - Literature synthesis - Trusted source + AI enhancement - Validation ongoing

Low Evidence / Avoid:

❌ Most direct-to-consumer symptom checkers - Poor accuracy ❌ Unvalidated chatbots for medical advice - Risk of misinformation ❌ “AI diagnoses everything” systems - Marketing > evidence ❌ Tools without peer-reviewed publications - Unproven claims

24.1 Introduction: Navigating the AI Tool Landscape

As of 2024, the FDA has cleared 520+ AI/ML-based medical devices, with hundreds more marketed without FDA oversight (clinical decision support, wellness applications). For physicians, the challenge isn’t finding AI tools—it’s identifying which ones actually work, have solid evidence, integrate into workflows, and provide clinical value.

This chapter provides: - Evidence-based tool recommendations by category - Specific product names and validation evidence - Implementation considerations - Cost and reimbursement information where available - Hands-on resources for evaluation

What this chapter is NOT: - Comprehensive product catalog (tools evolve rapidly) - Vendor endorsements (evidence-based assessments only) - Substitutes for your own due diligence

24.2 Category 1: Clinical Decision Support (CDS)

24.2.1 Traditional CDS (Pre-AI Era)

UpToDate - Type: Evidence-based clinical reference - AI Features: Recently adding AI literature synthesis, question answering - Evidence: Widely adopted, associated with improved outcomes in observational studies (Isaac, Zheng, and Jha 2012) - Cost: Institutional/individual subscriptions ($500-700/year individual) - Strength: Trusted source, regularly updated, comprehensive - Limitation: Not “AI” in modern sense, though adding AI features

DXplain (Massachusetts General Hospital) - Type: Differential diagnosis generator - Function: Enter findings → generates ranked differential - Evidence: Used since 1980s, educational tool primarily - Cost: Free for medical professionals - Strength: Broad knowledge base - Limitation: Doesn’t narrow differential without clinical judgment

Isabel Healthcare - Type: Differential diagnosis support - Function: Enter patient presentation → suggests diagnoses - Evidence: Some validation studies, primarily educational use - Cost: Subscription-based - Limitation: Accuracy variable, requires clinical interpretation

24.2.2 Modern AI-Enhanced CDS

Epic Sepsis Model - Type: EHR-integrated sepsis prediction - Function: Real-time risk score based on vital signs, labs - Evidence: CONTROVERSIAL - External validation showed 67% sensitivity (Wong et al. 2021) - Cost: Included with Epic EHR - Strength: Integrated workflow - Limitation: High false positive rates, mixed evidence for clinical benefit - Verdict: Use with caution, understand limitations

WAVE Clinical Platform (ExcelMedical) - Type: Continuous vital sign monitoring + early warning scores - Function: ICU/step-down monitoring, deterioration prediction - Evidence: Some validation in specific settings - Cost: Institutional licensing - Use case: Hospital early warning systems

24.3 Category 2: Diagnostic AI

24.3.1 Radiology AI (Comprehensive List)

24.3.1.1 Intracranial Hemorrhage Detection

Aidoc (Market Leader) - FDA Clearance: Yes - ICH, PE, C-spine fractures, pneumothorax, rib fractures - Evidence: Multiple validation studies, 1000+ hospital deployments - Performance: >95% sensitivity for ICH in validation - Workflow: Flags positive studies, notifies radiologists via mobile app - Cost: Per-scanner annual licensing (~$20-50K/year depending on volume) - Integration: PACS-integrated - Verdict: Strong evidence, widely deployed

Viz.ai - FDA Clearance: Yes - ICH, LVO stroke, pulmonary embolism, aortic dissection - Evidence: Published reduction in time-to-treatment for stroke (McLellan et al. 2022) - Unique feature: Care coordination platform (automated notifications to specialists) - Deployment: 1400+ hospitals - Cost: Institutional contracts - Verdict: Excellent evidence for stroke, expanding indications

RapidAI - FDA Clearance: Yes - Multiple stroke applications, PE - Specialty: Neuroradiology focus - Features: ASPECTS scoring, perfusion analysis, hemorrhage volume - Evidence: Strong validation for stroke applications - Verdict: Leading stroke AI platform

24.3.1.2 Chest X-Ray AI

Lunit INSIGHT CXR - FDA Clearance: Yes - Detection: Pneumothorax, nodules, consolidation, pleural effusion, cardiomegaly - Evidence: High sensitivity/specificity in trials - International: Strong deployment in Asia, Europe - Verdict: Well-validated chest X-ray AI

Oxipit ChestLink - FDA Clearance: Yes - pneumothorax detection - Performance: >95% sensitivity for PTX - Use case: ED triage, ICU - Verdict: Solid option for pneumothorax detection

qXR (Qure.ai) - Detection: 29 chest X-ray findings - Evidence: Validation studies in TB-endemic regions - International focus: Strong in India, emerging markets - Cost: Competitive pricing - Verdict: Good for resource-limited settings

24.3.1.3 Mammography AI

iCAD ProFound AI - FDA Clearance: Yes - Function: Breast cancer detection assistance - Evidence: Improves cancer detection rates in validation studies - Deployment: Widely used in US - Integration: Major mammography vendors - Verdict: Market leader, strong evidence

Lunit INSIGHT MMG - FDA Clearance: Yes - Evidence: Non-inferior to radiologists in trials - International: Strong European, Asian presence - Verdict: Well-validated alternative to iCAD

Hologic (Genius AI) - FDA Clearance: Yes - Integration: Native to Hologic equipment - Advantage: Seamless integration if using Hologic - Verdict: Good if already using Hologic systems

24.3.1.4 Other Imaging Modalities

Arterys (Cardiac MRI, CT Angiography) - FDA Clearance: Multiple - Function: Automated cardiac chamber quantification, vessel analysis - Evidence: Strong validation, time savings - Deployment: Academic medical centers, cardiology practices - Verdict: Leading cardiac imaging AI

HeartFlow FFR-CT - FDA Clearance: Yes - Function: CT-based fractional flow reserve (non-invasive) - Evidence: RCT evidence for reducing unnecessary catheterizations - Reimbursement: CPT codes established - Cost-effectiveness: Demonstrated in studies - Verdict: Excellent evidence, clinically impactful

24.3.2 Pathology AI

Paige Prostate - FDA Clearance: Yes (De Novo, 2021) - Function: Prostate biopsy cancer detection - Evidence: Prospective validation, improves detection of high-grade cancer (Pantanowitz et al. 2020) - Use case: Assists pathologists, reduces false negatives - Verdict: Strongest evidence for clinical pathology AI

PathAI - Function: Multiple pathology applications (GI, breast, prostate) - Status: Research collaborations, clinical deployments expanding - Evidence: Strong validation studies - Verdict: Promising, watch for FDA clearances

Proscia - Function: Digital pathology platform + AI modules - Use case: Workflow optimization, quantitative pathology - Verdict: Leading digital pathology infrastructure

24.3.3 Dermatology AI

3Derm - FDA Clearance: Yes - Function: Melanoma risk assessment - Use case: Primary care, dermatology triage - Evidence: Validation studies published - Limitation: Performance varies by skin type

SkinVision (Direct-to-consumer) - Function: Smartphone skin lesion assessment - Evidence: Variable validation - Concern: Consumer apps often lack rigorous validation (Freeman et al. 2020) - Verdict: Insufficient evidence for clinical recommendation

24.3.4 Ophthalmology AI

IDx-DR (Digital Diagnostics) - FDA Clearance: Yes (De Novo, 2018) - First autonomous AI diagnostic - Function: Diabetic retinopathy screening from retinal photos - Evidence: Prospective RCT, 87.2% sensitivity, 90.7% specificity (Abràmoff et al. 2018) - Deployment: Primary care, endocrinology, ophthalmology - Reimbursement: CPT 92229 (~$50-80) - Cost: Equipment + per-patient fees - Verdict: Gold standard medical AI - strongest evidence, proven deployment

EyeArt (Eyenuk) - FDA Clearance: Yes - Function: Diabetic retinopathy screening - Evidence: Comparable to IDx-DR - Verdict: Validated alternative

RetCAD (Altris AI, formerly 13th Bioscience) - FDA Clearance: Yes - AMD (age-related macular degeneration) - Function: AMD risk assessment - Verdict: Expanding beyond diabetic retinopathy

24.4 Category 3: Documentation and Ambient Scribe AI

24.4.1 FDA Note: These are NOT FDA-regulated (clinical decision support, not diagnostic)

Nuance DAX (Dragon Ambient eXperience) - Function: Ambient clinical documentation - AI listens, generates note - Evidence: High physician satisfaction, 50-70% documentation time reduction - Deployment: Thousands of physicians across specialties - Cost: ~$500-1000/physician/month - Integration: EHR-agnostic, integrates with major EHRs - Privacy: HIPAA-compliant, encrypted - Workflow: Physician reviews/edits AI-generated note before signing - Verdict: Market leader, strongest evidence for physician satisfaction

Abridge - Function: Clinical conversation recording + structured note generation - Unique: Patient-shareable visit summary - Evidence: High user satisfaction, expanding deployment - Cost: Competitive with DAX - Verdict: Strong alternative, patient engagement features

Suki - Function: Voice-enabled AI assistant for documentation - Features: Note generation, order placement, ICD/CPT code lookup - Evidence: Physician satisfaction, time savings - Deployment: Growing rapidly - Verdict: Feature-rich option

DeepScribe - Function: Ambient medical transcription + note generation - Evidence: Time savings, user satisfaction - Deployment: Primary care, specialty clinics - Verdict: Good option, especially for smaller practices

Freed AI - Function: Medical scribe AI - Evidence: User satisfaction - Cost: Free tier available, paid plans - Verdict: Low-cost option for solo/small practices

Implementation Considerations:

✅ Benefits: - Significant time savings (1-2 hours/day documentation) - Improved patient eye contact - Reduced burnout - After-hours documentation reduced

⚠️ Considerations: - Requires physician review (AI makes errors) - Patient consent for recording - Privacy/security (HIPAA-compliant vendors only) - Cost (ROI depends on time saved, productivity gains) - Learning curve (initial weeks slower as physician adapts)

24.5 Category 4: Literature Search and Synthesis

PubMed / MEDLINE (with AI enhancements) - Free, comprehensive - New features: AI-powered search refinement (limited) - Verdict: Still the gold standard, but time-consuming

Consensus (consensus.app) - Function: AI searches scientific papers, synthesizes findings - Use case: Quick literature review, evidence synthesis - Evidence: Growing adoption among researchers - Cost: Free tier, paid for advanced features - Verdict: Useful for rapid evidence gathering

Elicit (elicit.org) - Function: AI research assistant - finds papers, extracts key info - Use case: Literature review, research questions - Cost: Free tier, paid plans - Verdict: Helpful for systematic searches

Scite.ai - Function: Citation analysis - shows how papers cite each other (supporting, contrasting) - Use case: Evaluating strength of evidence, finding contradictory studies - Cost: Subscription - Verdict: Valuable for critical appraisal

ResearchRabbit - Function: Literature mapping, citation networks - Cost: Free - Verdict: Excellent for exploring research landscapes

Connected Papers - Function: Visual citation networks - Use case: Finding related papers - Cost: Free - Verdict: Great visualization tool

24.6 Category 5: Patient Communication

24.6.1 Patient Education

ChatGPT / GPT-4 (with extreme caution) - Capabilities: Generate patient education materials, explain diagnoses - Evidence: Can produce accurate information for common topics (Singhal et al. 2023) - Critical limitations: - Hallucinates (makes up plausible-sounding false information) - No access to patient-specific data - No liability/accountability - May generate outdated or incorrect guidance - Appropriate use: - Draft patient education materials (physician reviews/edits) - Simplify complex medical concepts (verify accuracy) - NOT for patient-specific medical advice - Verdict: Useful tool with physician oversight, NEVER autonomous patient advice

Google Med-PaLM 2 - Medical-specific LLM - Evidence: Better performance than GPT-4 on medical licensing exams - Status: Research access only, not publicly available - Verdict: Watch for clinical deployment

24.6.2 Symptom Checkers (Patient-Facing)

Ada Health - Function: Symptom assessment, triage guidance - Evidence: Variable accuracy (30-60% for correct diagnosis in top 3) - Use case: Patient triage (ED vs. urgent care vs. PCP) - Verdict: Triage tool, not diagnostic

Buoy Health - Function: Symptom checker + care navigation - Evidence: Validation studies ongoing - Partnerships: Major health systems integrating - Verdict: Promising for patient navigation

K Health - Function: AI symptom assessment + telemedicine - Model: Subscription-based primary care - Verdict: Integrated care model

Caution on Symptom Checkers: - Accuracy limited (patients may not describe symptoms accurately) - Liability unclear if patients rely on recommendations - Best use: Triage, not diagnosis - Physicians should be cautious recommending specific tools

24.7 Category 6: Specialty-Specific Tools

24.7.1 Cardiology

HeartFlow FFR-CT (covered above)

Caption Health (GE HealthCare) - FDA Clearance: Yes - Function: AI-guided cardiac ultrasound acquisition - Use case: Point-of-care echo by non-experts - Evidence: Enables accurate image capture by novices - Verdict: Democratizes cardiac ultrasound

Eko Analysis - Function: Digital stethoscope + AI murmur detection - Use case: Primary care, cardiology - Evidence: Detects valvular heart disease - Verdict: Useful screening tool

24.7.2 Oncology

Tempus - Function: Genomic analysis + treatment matching - Use case: Precision oncology - Evidence: Widely used, NCCN-cited - Verdict: Leading precision oncology platform

Foundation Medicine - Function: Comprehensive genomic profiling - Use case: Cancer treatment selection - Evidence: FDA-cleared assays - Verdict: Gold standard tumor profiling

IBM Watson for Oncology ❌ - Status: DISCONTINUED after failures - Lesson: Marketing ≠ clinical validity

24.7.3 Emergency Medicine

Viz.ai suite (covered above - stroke, PE)

Epic Deterioration Index - Function: Patient deterioration prediction - Evidence: Variable - some validation, implementation challenges - Cost: Included with Epic - Verdict: Use with caution, understand limitations

24.7.4 Anesthesiology

Medtronic GI Genius - FDA Clearance: Yes - Function: AI-assisted colonoscopy (polyp detection) - Evidence: Increases adenoma detection rate - Use case: GI procedures - Verdict: Improves polyp detection

24.8 Hands-On: Evaluating AI Tools for Your Practice

24.8.1 Step 1: Identify Clinical Need

Ask: - What problem am I trying to solve? - Is this a real workflow pain point? - Will AI solution improve patient outcomes, efficiency, or satisfaction?

24.8.2 Step 2: Evidence Review

Essential questions: - FDA-cleared? (Check FDA database: accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm) - Peer-reviewed publications? (PubMed search) - Prospective validation? (Not just retrospective) - External validation? (Multiple institutions, populations) - Performance in MY setting? (Demographics, EHR, workflow)

24.8.3 Step 3: Workflow Assessment

Integration: - EHR-integrated or standalone? - Number of clicks? - Time added or saved? - Who operates it? (Physician, MA, nurse?)

24.8.4 Step 4: Financial Analysis

Costs: - Licensing fees (annual, per-study, per-patient) - Hardware (servers, cameras, specialized equipment) - Personnel (training, IT support, clinical champions) - Maintenance and updates

ROI: - Time saved (value your time) - Reimbursement (CPT codes available?) - Quality metrics (value-based care bonuses) - Risk reduction (fewer malpractice claims) - Patient satisfaction (retention, referrals)

24.8.5 Step 5: Pilot Testing

Before full deployment: - Retrospective testing on YOUR data - Small pilot with limited users - Collect feedback (physician, patient, staff) - Measure impact (time, accuracy, satisfaction) - Identify failure modes

24.8.6 Step 6: Continuous Monitoring

Post-deployment: - Quarterly performance reviews - User feedback collection - False positive/negative tracking - Clinical outcome monitoring - Vendor support responsiveness

24.9 Red Flags: When to Avoid AI Tools

❌ No FDA clearance for diagnostic applications (wellness/CDS exceptions)

❌ No peer-reviewed publications (only vendor whitepapers)

❌ No external validation (only tested at vendor institution)

❌ Vendor refuses to share performance data (lack of transparency)

❌ Claims that seem too good to be true (“99.9% accuracy,” “replaces physicians”)

❌ Unclear data use policies (who owns data, how is it used)

❌ Poor customer references (other physicians had negative experiences)

❌ Overly complex integration (requires major workflow changes)

❌ No clear clinical value proposition (solution looking for problem)

24.10 Resources for Staying Current

FDA AI Device Database: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices

Medical AI Research: - npj Digital Medicine (Nature) - The Lancet Digital Health - JAMA Network Open (AI sections) - Radiology: Artificial Intelligence

Professional Organizations: - Society for Imaging Informatics in Medicine (SIIM) - American Medical Informatics Association (AMIA) - Radiological Society of North America (RSNA) AI sessions

Conferences: - RSNA (annual AI showcase) - HIMSS (health IT focus) - ML4H (Machine Learning for Health - NeurIPS workshop)

24.11 The Clinical Bottom Line

TipKey Takeaways

  1. Prioritize evidence: FDA clearance, peer-reviewed validation, prospective studies

  2. Start with proven applications: Diabetic retinopathy screening, ambient documentation, specific radiology tasks

  3. Evaluate for YOUR setting: External validation data, your patient population, your workflow

  4. Calculate real ROI: Time savings, quality metrics, reimbursement, risk reduction

  5. Pilot before full deployment: Test on your data, collect feedback, identify failures

  6. Avoid red flags: No evidence, no transparency, too-good-to-be-true claims

  7. Continuous monitoring essential: Performance can drift, vigilance required

  8. Patient communication matters: Transparency, consent, addressing concerns

  9. You remain responsible: AI is tool, liability stays with physician

  10. Field evolving rapidly: Stay current, re-evaluate tools regularly

Next Chapter: We’ll dive deep into Large Language Models (ChatGPT, GPT-4, Med-PaLM) for clinical practice—capabilities, limitations, and safe usage guidelines.

24.12 References