Article Figures & Data
Figures
- Figure 1
Annual number of devices approved/cleared by the US Food and Drug Administration that have an artificial intelligence or machine learning focus.
- Figure 2
List of spine surgery outcome prediction studies, 2014 to 2022. PROMs, patient-reported outcome measures.
- Figure 3
Type and frequency of commonly used artificial intelligence or statistical algorithms for predictive spine surgery modeling.
Tables
Database Dataset (US) No. of Patient Records Electronic health records 800 Million doctor visits annually 154,000,000,000 Medicare 83 Million patients 15,000,000,000 Private payer 177 Million patients 34,000,000,000 Wearable/implantable sensors 20% Of population 600,000,000 Medical imaging data 125 Million/y 125,000,000 Registries 80 Major, several hundred minor 5,000,000 HCUP and other 10 Million patients 1,900,000,000 Abbreviation: HCUP, Healthcare Cost and Utilization Project.
Digital EMR Scribe Supplier Type of AI Ambient Audio Recording Ambient Video Recording Connect to EMR System Time to Return Transcription Coding Services Practice Focus Robin16 Robin Healthcare CNN Yes Yes Yes 24 h Yes Orthopedics and spine Deep Scribe17 Deep Scribe CNN Yes No Yes Real time Yes Nonspecific DAX18 Nuance (Apple) CNN Yes No Yes h Unknown Nonspecific Abbreviations: AI, artificial intelligence; CNN, convolutional neural network; DAX, Dragon Ambient eXperience; EMR, electronic medical record.
In this issue
Jump to section
- Article
- Abstract
- Introduction
- Artificial vs Human Intelligence
- DeepMind and the CNN Moment
- Spine Surgery Heuristics
- How AI Plus Big Data Changes Spine Surgery Heuristics
- Big Data and Predictive Value
- AI as a Productivity Tool
- Categories of AI and the Law of Large Numbers
- Two Existential Issues: Explainability and Causal vs Correlational Decision Models
- Footnotes
- References
- Figures & Data
- Info & Metrics
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