---
title: "Why AI for Older Adults Requires Careful Scrutiny, a Geriatrician Warns"
id: "stat-news-3-stat-a-geriatrician-explains-why-ai-for-older-adults-deserves-careful-scrutiny"
canonical_url: "https://medichelpline.com/clinical-feed/stat-news-3-stat-a-geriatrician-explains-why-ai-for-older-adults-deserves-careful-scrutiny"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "STAT News"
source_url: "https://www.statnews.com/2026/09/18/geriatrician-explains-why-ai-for-older-adults-deserves-careful-scrutiny/?utm_campaign=rss"
published_at: "2026-09-18T08:30:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Why AI for Older Adults Requires Careful Scrutiny, a Geriatrician Warns
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/stat-news-3-stat-a-geriatrician-explains-why-ai-for-older-adults-deserves-careful-scrutiny
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** STAT News
- **Source URL:** [Original Journal Publication](https://www.statnews.com/2026/09/18/geriatrician-explains-why-ai-for-older-adults-deserves-careful-scrutiny/?utm_campaign=rss)
- **Published At:** 2026-09-18T08:30:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Doctors increasingly use **artificial intelligence** risk prediction models across medicine, often embedded in **electronic health records**, where their output can be easy to accept and act on. - James Deardorff, a geriatrician and assistant professor at UCSF, has developed models predicting outcomes for older adults, including mortality and need for nursing home care. - Deardorff emphasizes clinicians should evaluate both an algorithm’s overall performance and how it performs in subgroups such as older patients before relying on it in care decisions. - He wrote a commentary responding to a large JAMA Network Open analysis of Epic’s proprietary end-of-life prediction model, arguing that accurate predictions can still lead to harmful decisions depending on how they are used. - The commentary contrasts low-stakes uses (e.g., prompting open-ended goals-of-care conversations) with high-stakes applications (e.g., informing transplant priority), where the latter could have profound impacts on patients. - The STAT article reporting Deardorff’s views is behind the STAT+ paywall; the publicly available portion summarizes his main points and references the JAMA Network Open pieces but indicates the remainder of the story requires subscription to read. - Source details include the author (Katie Palmer), publication date (Sept. 18, 2026), and Deardorff’s affiliation (UCSF). Further specifics from the STAT+ article beyond the summarized commentary were not reported in the publicly available source.
## Clinical Analysis & Structured Key Points
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Test your knowledge with our new weekday mini crossword [ Start solving ](https://www.statnews.com/stat-mini-crossword/) [STAT Plus](https://www.statnews.com/stat-plus/)[Health tech](https://www.statnews.com/category/health-tech/) # A geriatrician explains why AI for older adults deserves careful scrutiny ## AI can help patients retain independence, but James Deardorff says good outcomes aren’t a given * [Manage alerts for this article](https://www.statnews.com/my-account/edit/#emails) * [Save this story for later](https://www.statnews.com/my-account/saved/) * [Share this article](https://www.statnews.com/2026/09/18/geriatrician-explains-why-ai-for-older-adults-deserves-careful-scrutiny/?utm_campaign=rss) ![nursing home](https://www.statnews.com/wp-content/uploads/2022/04/AP22039367701680-645x645.jpg) Michael Probst/AP ![](https://www.statnews.com/wp-content/uploads/2021/02/katie-p-avatar-small-80x80.png) By [Katie Palmer](https://www.statnews.com/staff/katie-palmer/) Sept. 18, 2026 Health Tech Correspondent ![](https://www.statnews.com/wp-content/uploads/2021/02/katie-p-avatar-small-80x80.png) [Katie Palmer](https://www.statnews.com/staff/katie-palmer/) 1. [](https://x.com/KatieMPalmer "X") katie.palmer@statnews.com Katie covers the impact of health technology on patients, clinicians, and businesses. Her stories explore the price tag of clinical AI, digital health at the FDA, and the boom in direct-to-consumer telehealth. Confidential tips can be sent on Signal at palmer.01. Across medicine, doctors are increasingly using artificial intelligence models to predict patient risk, from sepsis to falls to death. Often embedded directly into electronic health records, these predictions can be easy to incorporate into care — and their performance can be easily taken for granted. James Deardorff, a geriatrician and assistant professor in the division of geriatrics at the University of California San Francisco, has developed several models that aim to make predictions for older adults, from mortality to the need for nursing home care. In geriatrics, he says, it’s important for clinicians to be aware of both the performance of an algorithm — including in subgroups like older patients — and how to responsibly use its output. Advertisement This month, Deardorff penned a [commentary](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2853923?resultClick=3) on a large [analysis](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2853919) of Epic’s proprietary end-of-life prediction model published in JAMA Network Open, highlighting how a model can contribute to a poor outcome even if it’s accurate. If a patient’s one-year mortality prediction is used to prompt an open-ended conversation about the goals of care, he and his co-author wrote, there are few downsides. But if it’s used to inform a higher-stakes decision like transplant priority, the impacts could be profound. STAT+ Exclusive Story Already have an account? 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