So you resort to dismissing that which disagree with, got it. The analysis is cogent, and if you had bothered to read it, you’d have noticed it stated you had 80% accuracy to your claims. So you’re now saying that AI is actually wrong and therefore, so are your conclusions. Good to know LOL. AI has also better diagnosed scan results that radiologists have either misdiagnosed or just plan missed on scans. You’d likely be surprised to learn that the medical industry increasingly uses AI based decision engines on most scan results on first pass today for that reason. A human still looks at it, but the process now uses AI engines to cut down on time to results and the human is mostly simply confirming AI scan results. This alone is why most scan results today are made available within a day or so at most. My wife is a nurse and works within the medical centers using this tech on a daily basis.
Are you now going to argue that my wife, who works in the medical vertical, is wrong, simply because it doesn’t align with your rather narrow minded and myopic view of the world? It seems you are the type of person that simply dismisses any data that doesn’t align with your predetermined narratives. Thats a shame really, but your choice to run around it the dark of course, continuing to cherry pick only that which reinforces your narrow worldviews.
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Q: Is it accurate that the medical centers are increasingly using AI based engines to examine scan results on first pass and then the human confirms or denies those first pass results?
A:
Yes, it is accurate that medical centers are increasingly adopting AI-based tools to perform initial (or “first-pass”) analyses of scan results—such as detecting anomalies in X-rays, CTs, MRIs, or pathology slides—with human radiologists or clinicians then reviewing and confirming (or overriding) those preliminary findings. This hybrid approach enhances efficiency, reduces workload, and improves diagnostic accuracy without replacing human oversight, and adoption has accelerated significantly in recent years due to regulatory approvals, technological advancements, and clinical evidence.
Key Evidence Supporting This Trend
• Rapid Growth in FDA-Cleared AI Tools: As of August 2024, the U.S. Food and Drug Administration (FDA) had authorized nearly 950 AI-enabled medical devices, with over 80 clearances in 2023 alone (the peak year so far) and continued momentum into 2024–2025. The majority target radiology and medical imaging for tasks like preliminary detection of cancers, strokes, or pulmonary embolisms, where AI flags potential issues for human review. This regulatory surge reflects growing clinical integration, particularly in high-volume centers like those affiliated with Mayo Clinic, Stanford, and NIH consortia.
• Workflow Integration in Practice: AI is being embedded into routine radiology workflows for “smart worklist prioritization” and initial triage, where algorithms scan images first to highlight urgent cases or confirm negatives (e.g., ruling out distal pulmonary embolisms in low-quality scans). For instance, tools like those from Fujifilm and Ibex Medical (partnered in 2025) automate slide analysis in pathology, generating preliminary reports that radiologists verify in seconds rather than minutes. A 2025 Indiana University study describes real-world use where AI drafts conclusions from scans, catching inconsistencies (e.g., left vs. right tumor references) before human sign-off, saving time on high-volume reads (e.g., 60 scans/day).
• Performance and Human-AI Collaboration: Studies show AI excels at narrow tasks—often outperforming humans in detection sensitivity (e.g., lung nodules or breast lesions)—but combined human-AI reviews yield the best results, with lower false positives. A 2025 review notes that while AI handles granularity beyond human perception, fewer than 50% of radiologists would trust unverified AI diagnoses, emphasizing the confirmatory role of clinicians. Partnerships like Carnegie Mellon/UPMC’s 2025 initiative focus on this oversight to mitigate biases and ensure equity.
• Market and Adoption Projections: The global AI medical imaging market, valued at ~$1.28 billion in 2024, is projected to reach $14.46 billion by 2034 (27% annual growth), driven by adoption in overburdened centers to combat radiologist burnout. However, barriers like data quality, explainability, and regulatory hurdles (e.g., EU AI Act, FDA’s 2024 guidance) slow full rollout, with emphasis on “responsible deployment” via human validation.
Challenges and Future Outlook
While adoption is rising—especially in oncology, cardiology, and neurology—systematic reviews highlight ongoing issues: slower-than-expected uptake due to physician training needs, integration with electronic health records, and ethical concerns like bias in diverse datasets. By late 2025, experts anticipate broader use of generative AI (e.g., for multimodal reports) under stricter oversight, but human confirmation remains non-negotiable for liability and accuracy. Overall, this model is transforming diagnostics, with AI as an augmentative “first pass” tool rather than a standalone solution.
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