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Research Article: Machine learning-assisted prognosis prediction and surgical decision-making for glioblastoma: perceived benefits and concerns of patients, caregivers, and neurosurgeons

Date Published: 2026-07-02

Abstract:
It is becoming more common for machine learning (ML) models to aid prognostication and clinical decision-making, including for glioblastoma (GBM) cases. However, there is a lack of empirical research on how end-users view potential benefits and risks of implementing such models in clinical practice. This study examines the perspectives of GBM patients ( n =?13), their caregivers ( n =?14), and neurosurgeons treating GBM ( n =?15) about an ML model designed to predict GBM patient prognosis and inform surgical decisions. Participants completed interviews that were audio-recorded, professionally transcribed, and coded by the study team. All three groups thought a major benefit of the ML model was its ability to take into account a large amount and scope of patient data, which could help facilitate communication and decision-making among patients and neurosurgeons when planning treatment strategies or end-of-life care. Participants also expressed concerns about potential inaccuracies or biases in model output, and shared unease about the possibility of the model replacing a neurosurgeon’s clinical judgment entirely. Some patients and caregivers expressed concern about the model being in early stages of development and about how the delivery of ML-informed prognostic information could cause patients to lose hope or become confused about important prognostic or surgical information. This study highlights the value of engaging multiple stakeholders and triangulating their perspectives when developing ML models to support clinical decision-making. While ML models show great promise in synthesizing large amounts of data and supporting decision-making, biases in the data used to train these models and over-reliance on their predictions risk negatively affecting patient outcomes.

Introduction:
It is becoming more common for machine learning (ML) models to aid prognostication and clinical decision-making, including for glioblastoma (GBM) cases. However, there is a lack of empirical research on how end-users view potential benefits and risks of implementing such models in clinical practice.

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