Chase Walton

Importance: Patients increasingly use artificial intelligence (AI) for health-related conversations, but evaluation measures largely reflect clinician judgment. Objective: To develop and preliminarily evaluate a patient instrument assessing the quality of patient–AI health conversations. Design: Cross-sectional survey conducted June 5-14, 2026, following patient-partnered instrument development and cognitive interviews. Setting: An anonymous online survey. Participants: A convenience sample of adults with prior use of large language models for personal health-related queries, recruited from an academic medical center-affiliated Insight Panel and patient peers. Exposures: Participants’ own prior health-related conversations with AI tools of their choosing. Main Outcomes and Measures: The Patient-AI Rating instrument comprised 12 communication-quality and patient engagement items rated from 1 to 5 and a separately scored patient-perceived-harm item. Internal consistency was assessed using Cronbach α, with sensitivity analyses using first ratings only for those using the tool to rate multiple conversations and participant-mean profiles. We examined item–total correlations, inter-item structure, and between- and within-participant variation. Results: Fourteen participants contributed 46 complete ratings; among 13 providing demographics, median age was 63 years (IQR, 53-70 years), and 8 (62%) reported female gender. Participants provided a median of 2 ratings (range, 1-17). Cronbach α was 0.88 (95% CI, 0.83-0.93) across all ratings, 0.85 using first ratings only, and 0.92 using participant-mean profiles. Corrected item–total correlations ranged from 0.42 to 0.72; mean inter-item correlation was 0.40. No item deletion improved Cronbach α. The mean quality score was 4.43 (SD, 0.45). Among 9 participants contributing multiple ratings, the intraclass correlation coefficient was 0.45, suggesting inter-conversation variability. No perceived harm was reported in 44 of 46 ratings (96%); the harm item correlated minimally with the instrument total (r = 0.09). Conclusions and Relevance: In this small cross-sectional study of engaged patients, the Patient-AI Rating instrument showed promising internal consistency and variation across rated conversations. These findings support further evaluation of patient-reported communication quality alongside clinician-based measures. Validation in larger, more diverse populations, including assessment of construct and criterion validity, is needed before broader use. Key Points: Question: Can a patient instrument measure the quality of patients’ health-related conversations with artificial intelligence (AI)? Findings: In this cross-sectional study, 14 participants provided 46 ratings of their health-related AI conversations using a 12-item scale and a perceived-harm item (the Patient AI Rating (PAIR) instrument). The patient metric showed good internal consistency, and scores varied both by raters and across conversations rated by the same participant. Meaning: The PAIR instrument offers a preliminary approach to incorporate patient perspective and experience into AI evaluation – a measure largely missing from current AI assessment; larger studies are needed to establish validity and generalizability.

List of diagnostic codes for cancer ascertainment in the Medical Expenditure Panel Survey.

This file contains a key resources table that includes details of key experimental materials and supplies used in this study.

Metabolic changes in old WT T-cells as measured by flux metabolomics.

Different therapeutic approaches utilizing the superiority of 3xTg T-cells in resisting tumor growth and progression.

Patients with Alzheimer's disease (AD) have a decreased incidence of cancer, with a cross-sectional analysis of a nationwide sample of adults finding 21-fold higher odds of cancer diagnosis in non-AD individuals compared with those with AD. In this study, we demonstrated that mitochondrial localization of AD-associated amyloid-β precursor protein (APP) and its cleavage product amyloid-β 40, but not mutant APP that lacks a mitochondrial localization signal, inhibits lipid stress–mediated hyperactive mitophagy in aging T cells, improving their antitumor functions. Growth of melanoma xenograft or carcinogen-induced oral cancer models was highly reduced in AD mice. Additionally, adoptive cell transfer–based immunotherapy using aging T cells isolated from AD mice suppressed tumor growth. The metabolic signature of stress-dependent mitophagy in T cells showed fumarate depletion, which was linked to decreased succination of Parkin and enhanced mitochondrial damage. Mechanistically, APP interaction with the TOMM complex at the outer mitochondrial membrane attenuated trafficking of ceramide synthase CerS6 to mitochondria in aging AD T cells, preventing ceramide-dependent mitophagy. Thus, APP restored mitochondrial fumarate metabolism and Parkin succination, improving antitumor functions of AD T cells in vitro and in vivo. Exogenous fumarate supplementation or healthy AD mitochondria transfer functionally mimicked the AD/APP phenotype in aging T cells, enhancing their antitumor activity to control tumor growth. Moreover, T cells isolated from aging donors showed elevated mitophagy with fumarate depletion, which was restored in T cells isolated from age-matched patients with AD. Together, these findings show that AD protects T cells against ceramide-dependent mitophagy and fumarate depletion to enhance antitumor functions.Significance:The reduced cancer risk in Alzheimer's disease patients is mediated by the amyloid-β 40 peptide, which inhibits aging-dependent mitophagy in T cells to improve antitumor immunity.See related commentary by Bieberich, p. 3579

ATAD3 shuttles CerS6 to the mitochondria of aging T-cells and mitochondrial APP blocks this effect.

T-cells from human AD patients with cancer show an increase in APP levels, a decrease in mitophagy, and an increase in cytokine production.

Demographic characteristics of human APP-high vs APP-low samples.

List of diagnostic codes for cancer ascertainment in the Medical Expenditure Panel Survey.