Evidence since (and even before) late 2022 shows that AI models for tumor detection trained on large proprietary image corpora have indeed been cleared by the FDA relatively quickly via existing pathways.

Examples in breast cancer imaging

• Lunit INSIGHT MMG, an AI system for breast cancer detection, was trained on over 240,000 mammography cases including up to 50,000 with breast cancer and received FDA 510(k) clearance in November 2021—before Chamath’s comment. This is exactly the kind of large‑corpus, tumor‑classification AI he described, cleared via the standard 510(k) pathway, not a special new regime. (lunit.io)
• Therapixel’s MammoScreen, an AI-based breast cancer screening aid, received 510(k) clearance in 2020 and a second 510(k) in 2021 extending it to 3D tomosynthesis, again after multi‑reader clinical studies but through normal device pathways. (therapixel.com)
• DeepHealth/RadNet’s mammography AI products (Saige‑Q and later SmartMammo/SmartMammo Dx) also obtained 510(k) clearances, first in 2012 and then expanded indications and system compatibility in 2022 and 2024, illustrating repeated use of the same regulatory route for breast imaging AI. (diagnosticimaging.com)

Use of existing FDA pathways and speed

• Systematic reviews of FDA AI/ML-enabled devices show that by October 2023 there were ~691 such devices authorized, with about three‑quarters in radiology and the vast majority cleared via the traditional 510(k) pathway based on substantial equivalence rather than bespoke rules. (mdpi.com)
• By August 2024, FDA’s own updates report 950 AI/ML-enabled medical devices cleared or approved, again emphasizing standard pathways (510(k), De Novo, PMA) for AI tools, especially in imaging. (fda.gov)
• A study of AI/ML-enabled surgical devices found average FDA review times of roughly 142 days (~4.7 months), in line with typical 510(k) timeframes and supporting the characterization of these pathways as relatively fast compared with full Premarket Approval processes. (asc-abstracts.org)

Assessment vs. the prediction Chamath’s claim had two parts:

1. Such AI models could be approved using existing regulatory pathways. This is clearly borne out: AI mammography/tumor‑detection systems have repeatedly obtained 510(k) clearances without the FDA needing to create a fundamentally new pathway.
2. Those pathways allow approval “very quickly.” While “very quickly” is subjective, the empirical review times of a few months for 510(k) AI devices, versus years for full PMA processes, plus the rapid growth to hundreds of cleared radiology AI tools, support that the process is relatively fast in regulatory terms.

Because multiple AI systems closely matching his example (breast cancer image classifiers trained on very large datasets) have gained FDA clearance through existing, comparatively rapid pathways, the prediction that this kind of AI would be able to get FDA approval relatively quickly via existing mechanisms is best judged as right.