The report, The Promise of AI/ML in Healthcare, is the most comprehensive report published on this rapidly evolving market with nearly 120 vendors discussed. The report explores opportunities, trends, and the rapidly evolving landscape for vendors, tracing the evolution from early artificial intelligence/machine learning (AI/ML) use in medical imaging to today’s rich array of vendor solutions in medical imaging, business operations, clinical decision support, research and drug development, patient-facing applications, and more.
The report also reviews types and applications of AI/ML, explores the substantial challenges of health data collection and use, and considers issues of bias in algorithms, ethical and governance considerations, cybersecurity, and broader implications for business.
The market today can be divided into five separate categories based on the need being addressed by AI/ML: Hospital operations, Population health management, Clinical decision support, Research and drug development, and Consumer facing tools. Each of these categories is explored on its own and in the greater context of market forces that are driving innovation and adoption of these tools.
Provider organizations will find this report offers deep insight into current and forthcoming solutions that can help support business operations, population health management, and clinical decision support. Current and prospective vendors of AI/ML solutions and their investors will find this report’s overview of the current market valuable in mapping their own product strategy. Researchers and drug developers will benefit from the discussion of current AI/ML applications and future possibilities in precision medicine, clinical trials, drug discovery, and basic research.
Table of Contents
EXECUTIVE SUMMARY
1. KEY TAKEAWAYS
2. WHY AI/ML NOW?
- Health Data and AI
- Types of AI/ML
- First Movers in Healthcare
3. AI IN HEALTHCARE: BURGEONING OPPORTUNITIES, COUNTLESS VENDORS HOSPITAL OPERATIONS
- Revenue Cycle Management (RCM)
- Fraud
- Administration and Supply Chain
- Patient Safety
4. POPULATION HEALTH MANAGEMENT
- Risk Stratification
- Population/Care Management Tools
- Patient Engagement
5. CLINICAL DECISION SUPPORT (CDS)
- Clinical Documentation
- Medical Imaging and Pathology
6. EHR VENDORS’ AI SOLUTIONS RESEARCH, DRUG DEVELOPMENT, AND DISCOVERY
- Precision Medicine
7. PATIENT/CONSUMER-FACING APPLICATIONS
- Laboratory Testing
- Substance Abuse and Mental Health
- Patient Bots and Symptom Checkers
- Voice Assistants
- Wearables
8. EVOLUTION OF THE MARKET
- Current Market Trends and Real-World Adoption
- Bifurcation in the Market
- AI in the Cloud
9. CONCLUSIONS AND RECOMMENDATIONS
APPENDIX: PATIENT SAFETY ISSUES
ABOUT THE AUTHOR
TABLES AND FIGURES
EXECUTIVE SUMMARY
KEY TAKEAWAYS
WHY AI/ML NOW?
Figure 1: Patent Applications by Field
Figure 2: Taxonomy of Machine Learning Models
Figure 3: AI Applications
Figure 4: Timeline of Significant AI Advances in Healthcare
AI IN HEALTHCARE: BURGEONING OPPORTUNITIES, COUNTLESS VENDORS
Figure 5: IT Vendors Providing AI-based Solutions
AI IN HOSPITAL OPERATIONS
Figure 6: Business Operations Application Areas
Table 1: AI Solution Vendors for Hospital Operations
Figure 7: Healthcare Industry Adoption of Fully Electronic Business Transactions
Table 2: Average Cost and Savings Opportunities for Commercial Health Plans (2015)
Figure 8: Change Healthcare Claims Cycle/RCM platform
Figure 9: OSI Model
Figure 10: CloudFace Data Sources and Outputs
POPULATION HEALTH MANAGEMENT
Table 3: Solutions Using AI for PHM
Figure 11: Population Health Management Application Areas
Figure 12: Health Catalyst AI Implementation Map
Figure 13: Orion Health PHM platform
CLINICAL DECISION SUPPORT (CDS)
Table 4: AI Solution Vendors for Clinical Support
Figure 14: Clinical Decision Support Application Areas
EHR VENDORS’ AI SOLUTIONS
Figure 15: EHR AI analytics applications
RESEARCH, DRUG DEVELOPMENT, AND DISCOVERY
Figure 16: Research, Drug Development, and Discovery Application Areas
Table 5: AI Solution Vendors for Research
PATIENT/CONSUMER-FACING APPLICATIONS
Figure 17: Patient-Facing AI Application Areas
Table 6: AI Solution Vendors for Patient/Consumer Engagement
EVOLUTION OF THE MARKET
Figure 18: FDA Approvals of AI-Based Algorithms in Medicine
Figure 19: Evolution of Validation Stages for AI/ML
Figure 20: Adoption Timeline
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