Οι ακτινολόγοι για την ώρα δεν κινδυνεύουν
Radiology is a field optimized for human replacement, where digital inputs, pattern recognition tasks, and clear benchmarks predominate. In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’. If the most extreme predictions about the effect of AI on employment and wages were true, then radiology should be the canary in the coal mine.
But demand for human labor is higher than ever. In 2025, American diagnostic radiology residency programs offered a record 1,208 positions across all radiology specialties, a four percent increase from 2024, and the field’s vacancy rates are at all-time highs. In 2025, radiology was the second-highest-paid medical specialty in the country, with an average income of $520,000, over 48 percent higher than the average salary in 2015.
Three things explain this. First, while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions. Second, attempts to give models more tasks have run into legal hurdles: regulators and medical insurers so far are reluctant to approve or cover fully autonomous radiology models. Third, even when they do diagnose accurately, models replace only a small share of a radiologist’s job. Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
Εξαιρετικά ενδιαφέρον κείμενο στο Works in Progress για τις δυσκολίες που βρίσκει η εφαρμογή της τεχνητής νοημοσύνης στον πραγματικό κόσμο. Χρειαζόμαστε καλύτερα μοντέλα, καλύτερα δεδομένα, καλύτερες διαδικασίες, διαφορετική νομοθεσία. Χρειάζεται χρόνος. Δείτε και τις σημειώσεις των Kapoor και Narayanan.