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AI in radiology accuracy statistics

Analyzed across ChatGPT, Perplexity & Google AIO
Analyzed 11/15/2025

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Brand Performance Across AI Platforms
All 5 brands referenced across AI platforms for this prompt
RSNA
3
2
Sentiment:
Score:95
YOLOv11
2
0
Sentiment:
Score:68
Qure.ai
0
1
Sentiment:
Score:58
4RamSoft
0
2
Sentiment:
Score:55
5Icahn School of Medicine at Mount Sinai
1
0
Sentiment:
Score:55
Referenced Domains Analysis
All 17 domains referenced across AI platforms for this prompt
ChatGPT
Perplexity
Google AIO
#1pmc.ncbi.nlm.nih.gov faviconpmc.ncbi.nlm.nih.gov
ChatGPT:
0
Perplexity:
1
Google AIO:
6
7
#2ramsoft.com faviconramsoft.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2
#3diagnosticimaging.com favicondiagnosticimaging.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2
#4pubmed.ncbi.nlm.nih.gov faviconpubmed.ncbi.nlm.nih.gov
ChatGPT:
1
Perplexity:
0
Google AIO:
1
2
#5mccormick.northwestern.edu faviconmccormick.northwestern.edu
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2

AI Search Engine Responses

Compare how different AI search engines respond to this query

ChatGPT

2470 Characters

BRAND (6)

精选行业Query
RamSoft
RSNA
Qure.ai
Icahn School of Medicine at Mount Sinai
YOLOv11

SUMMARY

ChatGPT provides an educational overview of AI accuracy in radiology with specific examples from lung cancer detection and chest X-ray interpretation studies. The response mentions improved radiologist performance with AI assistance and cites research from Finland showing AI correctly identified 36.4% of cases with no significant findings. The content appears to be cut off mid-sentence, suggesting incomplete information delivery.

Perplexity

2472 Characters

BRAND (6)

精选行业Query
RamSoft
RSNA
Qure.ai
Icahn School of Medicine at Mount Sinai
YOLOv11

SUMMARY

Perplexity delivers a comprehensive analytical breakdown of AI accuracy statistics in radiology, presenting detailed sensitivity and specificity data from multiple studies. Key findings include AI systems averaging 84% sensitivity and 61.5% specificity, with notable performance variations between AI alone versus radiologist-AI combinations. The response emphasizes quantitative metrics and systematic review data.

Google AIO

756 Characters

BRAND (6)

精选行业Query
RamSoft
RSNA
Qure.ai
Icahn School of Medicine at Mount Sinai
YOLOv11

SUMMARY

Google AIO takes a comparative approach, highlighting the variability in AI performance across different radiology tasks. The response notes that while some studies show AI matching or exceeding human performance, others demonstrate superior human performance due to different objectives. It mentions specific examples of fracture detection and chest X-ray analysis, emphasizing the nuanced differences between AI and human clinician capabilities.

Strategic Insights & Recommendations

Dominant Brand

RSNA appears most frequently across platforms as a key research source, though no specific AI radiology product brands dominate the responses.

Platform Gap

Perplexity provides the most detailed quantitative statistics while ChatGPT offers broader educational context and Google AIO focuses on comparative performance analysis.

Link Opportunity

All platforms provide substantial link opportunities with ChatGPT offering 5 links, Google AIO providing 14 links, and Perplexity including 8 links to research sources.

Key Takeaways for This Prompt

AI accuracy in radiology varies significantly by specific application and imaging type, with no universal performance standard.

Radiologist-AI combination consistently outperforms either AI systems or radiologists working independently.

AI systems typically show high sensitivity but lower specificity compared to human radiologists in most studies.

The field lacks standardized evaluation metrics, making direct comparisons between studies and systems challenging.

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