Patient-initiated conversational AI between appointments in breast cancer care: a multidisciplinary clinical and policy perspective
5 affiliations
- 1Breast Unit, West Lisbon Local Health Unit
- 2NOVA Medical School
- 3Department of Psychiatry and Mental Health, Arco Ribeirinho Local Health Unit
- 4Department of Anaesthesia and Intensive Care, University Hospitals of Morecambe Bay NHS Foundation Trust
- 5WHO Collaborating Centre for Public Health Education and Training, Imperial College London
Abstract
General-purpose conversational artificial intelligence (AI) is increasingly used for health-related questions, including questions relevant to cancer care, although breast-cancer-specific prevalence and behavioural effects remain poorly characterised. Most evaluations ask whether an AI answer is accurate. That is necessary but incomplete. Clinically consequential effects may arise when a fluent, apparently personalised exchange changes a treatment preference, delays contact with the clinical team, reinforces reassurance-seeking, validates non-adherence, or converts population guidance into an individual-sounding instruction. We conceptualise this as a parallel pathway: an AI-mediated sequence of questions, answers and actions that is usually invisible to the treating team, absent from the medical record and unattached to professional responsibility, yet capable of re-entering formal care through its downstream consequences. We distinguish model error from pathway error and propose traceability, reversibility, and returnability to accountable care as safety properties beyond answer accuracy. Across four clinical domains, we propose distinct pathway failure modes: premature preference closure in surgical decision-making; escalation closure during treatment toxicity; repetitive reassurance-seeking and over-reliance in psycho-oncology; and loss of conditionality in perioperative recovery and lifestyle advice. These are hypothesis-generating mechanisms rather than empirically established dominant patterns. We propose a five-step clinical response - ASK, RECONSTRUCT, CLASSIFY, CHECK and RETURN - based on non-judgemental inquiry, proportionate documentation and explicit return-to-care boundaries. Regulation can govern products and deployed systems, but it cannot by itself reveal patient-initiated consumer AI use. Clinical services therefore need governance for the pathway as well as for the technology.
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BibTeX
@article{sidiropoulou2026patientinitiated,
title = {{Patient-initiated conversational AI between appointments in breast cancer care: a multidisciplinary clinical and policy perspective}},
author = {Zacharoula Sidiropoulou and Guida da Ponte and Sunil Kumar and Vasco Fonseca},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1981490},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1981490/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Patient-initiated conversational AI between appointments in breast cancer care: a multidisciplinary clinical and policy perspective
AU - Zacharoula Sidiropoulou
AU - Guida da Ponte
AU - Sunil Kumar
AU - Vasco Fonseca
PY - 2026
DA - 2026/10/26
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1981490
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1981490/full
AB - General-purpose conversational artificial intelligence (AI) is increasingly used for health-related questions, including questions relevant to cancer care, although breast-cancer-specific prevalence and behavioural effects remain poorly characterised. Most evaluations ask whether an AI answer is accurate. That is necessary but incomplete. Clinically consequential effects may arise when a fluent, apparently personalised exchange changes a treatment preference, delays contact with the clinical team, reinforces reassurance-seeking, validates non-adherence, or converts population guidance into an individual-sounding instruction. We conceptualise this as a parallel pathway: an AI-mediated sequence of questions, answers and actions that is usually invisible to the treating team, absent from the medical record and unattached to professional responsibility, yet capable of re-entering formal care through its downstream consequences. We distinguish model error from pathway error and propose traceability, reversibility, and returnability to accountable care as safety properties beyond answer accuracy. Across four clinical domains, we propose distinct pathway failure modes: premature preference closure in surgical decision-making; escalation closure during treatment toxicity; repetitive reassurance-seeking and over-reliance in psycho-oncology; and loss of conditionality in perioperative recovery and lifestyle advice. These are hypothesis-generating mechanisms rather than empirically established dominant patterns. We propose a five-step clinical response - ASK, RECONSTRUCT, CLASSIFY, CHECK and RETURN - based on non-judgemental inquiry, proportionate documentation and explicit return-to-care boundaries. Regulation can govern products and deployed systems, but it cannot by itself reveal patient-initiated consumer AI use. Clinical services therefore need governance for the pathway as well as for the technology.
SN - 2234-943X
ER -