Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology

A narrative review of artificial intelligence and patient-reported outcomes in neuro-oncology concludes that machine learning and large language models could help identify symptoms, model longitudinal changes and support clinical decisions for people with brain tumors, but evidence of clinical benefit remains limited. The review included 53 eligible articles selected from searches of PubMed/MEDLINE, Embase and Google Scholar for English-language work published from January 2015 through April 2026, supplemented by reference-list reviews. Its scope was symptom assessment and patient-reported outcomes across the care continuum, not AI applications to imaging, pathology or genomics.

Most neuro-oncology studies described technical performance, feasibility, retrospective analyses or planned work rather than demonstrated improvements in patient outcomes. Patient-reported outcomes, or PROs, are information reported directly by patients about symptoms, functioning and quality of life, without interpretation by a clinician. The review identifies pain, communication difficulty, perceived cognition, seizures, symptomatic adverse events, physical functioning and role functioning as priority constructs for high-grade glioma trials and clinical care.

Clinician-rated measures, including the Karnofsky Performance Status, Neurologic Function Score and Common Terminology Criteria for Adverse Events, remain central in neuro-oncology, while the Mini-Mental State Examination and Montreal Cognitive Assessment provide performance-based cognitive screening. These measures complement rather than replace PROs. Studies cited in the review found that clinicians can underestimate symptom frequency and severity, whereas cognitive impairment, aphasia and reduced insight can compromise self-reporting.

Caregiver proxies can provide information, but their interpretation introduces additional complexity. The quality of PRO data therefore constrains what AI systems can learn. Missing responses, selection bias, inconsistent assessment timing and disagreement between clinicians and patients can distort symptom trajectories, estimated treatment effects and model predictions.

Questionnaire completion commonly declines with respondent burden, clinical deterioration and logistical barriers. In a phase III trial of prophylactic cranial irradiation in extensive-stage small cell lung cancer, fewer than half the expected quality-of-life forms were returned at 12 months. Attrition is especially consequential because patients whose condition is worsening are more likely to stop responding, potentially reducing statistical power and biasing results.

The review notes that standardized schedules, electronic collection, reminders, staff education, compliance monitoring and appropriate methods for handling missing data may improve data quality, while each must accommodate differences in neurological status, communication and access. Evidence that frequent symptom monitoring can improve outcomes is strongest outside neuro-oncology. In a single-institution randomized trial involving 766 patients receiving chemotherapy for metastatic cancers, electronic PRO monitoring with automated nurse alerts reduced emergency-department visits, prolonged time on chemotherapy and improved one-year survival.

The PRO-TECT cluster randomized trial, conducted across 52 practices with 1,191 patients, reported that weekly electronic PRO surveys improved physical function, symptom control and health-related quality of life at three months in diverse community oncology settings. Brain-tumor evidence has largely remained at the feasibility stage. A glioblastoma smartphone-app pilot showed that longitudinal health-outcome tracking could include caregiver-proxy entries, while a separate study involving 43 patients with glioblastoma found that electronic PROMIS and EORTC assessments could be completed in a clinical setting.

These studies demonstrate feasibility rather than the clinical benefits reported in general oncology. Classical machine learning offers a way to analyze large and heterogeneous longitudinal PRO datasets by learning patterns from data rather than following fixed rules. Early work predicting health-related quality of life after low-grade brain-tumor resection used support vector machines and random forests; the classifiers achieved areas under the curve above 0.90 for most symptom outcomes using routine perioperative inputs.

A systematic review of machine-learning approaches to cancer symptom prediction found that temporal and longitudinal modeling were underrepresented and that neuro-oncology was absent. Natural language processing can extract structured information from free-text clinical notes, another large and largely untapped part of the medical record, but oncology-specific applications in neuro-oncology remain limited. Many such systems also require extensive manual rule development and domain-specific expertise, which can restrict adaptability across settings.

Large language models may extend the range of data that can be processed. In an analysis of 51,541 neuro-oncology clinical notes from an electronic health record, GPT-4o extracted nine symptom domains with mean sensitivity and specificity of 0.97. It identified an average of 3.92 symptoms per note, and reported symptom prevalence was substantially higher than in comparable clinical trials.

Patients receiving immune checkpoint inhibitors had the highest symptom burden, illustrating how automated extraction can provide treatment-stratified observations that complement prospective PRO instruments. The analysis did not establish that extracted symptoms can prospectively detect deterioration in primary brain-tumor patients or serve as substitutes for patient reports. That distinction is fundamental: a PRO originates directly from the patient, whereas an LLM-derived interpretation is an AI-derived measure and should be labeled and evaluated separately.

Broader assessments of patient-reported outcome models indicate weaknesses in validation and implementation. A 2024 systematic review of 52 oncology studies found that fewer than 8% of models had been externally validated and fewer than 6% shared reproducible code, despite a roughly twentyfold increase in machine-learning publications using PRO data between 2011 and 2021. A separate 2025 scoping review of 94 studies using PROM data to predict outcomes across clinical fields identified only 10 external validations and three studies that had reached implementation.

It also found inconsistent handling of missing data and limited patient or clinician involvement in model development. Because the reviews covered different scopes, they do not demonstrate a direct time trend. They do show that analytical performance has often been assessed without replication in independent settings.

Neuro-oncology faces additional constraints from disease heterogeneity, modest sample sizes and symptoms that patients may be unable to report, including seizures and neglect. Caregiver reports, wearable data and clinical narratives can supply complementary information. Integrating predictive systems into care presents separate challenges from developing them.

In a review of cancer symptom-management clinical decision-support systems, only 25.5% were integrated into electronic health records and 18.2% incorporated AI, with most relying on rules rather than predictive models. The review cites evidence favoring standardized terminology, high-yield alerts and a defined response whenever an alert is issued, because frequent low-specificity warnings can produce alert fatigue. Decisions based on unreliable data, or decisions that could cause harm if made incorrectly, may require clinician verification.

A modular autonomous workflow using five LLM agents to identify cognitive concerns in unstructured notes performed comparably to, but did not outperform, an expert-refined workflow on an independent validation set. Its inspectable structure made prompt refinement easier to review. In neuro-oncology, a glioblastoma remote-monitoring study registered in 2023 pairs Apple Watch biometric data with patient- or caregiver-entered symptoms, while a French glioma study registered in 2026 will evaluate a platform for neurologic symptoms, treatment tolerance and quality of life.

These protocols describe planned research and do not establish efficacy. Ethical requirements are particularly consequential for patients whose cognition or decision-making capacity may change over time. Informed consent and clear communication are needed about when AI is used, what information it processes, how its output could affect care and what alternatives exist.

A clinician should be able to inspect the original patient or proxy report, identify its source and review clinically consequential interpretations in context. If a large language model infers a symptom from a conversation, that output must not be presented as a patient-reported outcome. Privacy and security obligations also vary by jurisdiction.

In the United States, a provision of the 21st Century Cures Act can exclude certain clinician-facing decision-support functions from the definition of an FDA-regulated medical device when specified criteria are met, including independent clinician review of the basis for a recommendation; this is not a blanket exemption for AI or electronic-health-record tools. In the European Union, the General Data Protection Regulation governs personal and health-data processing, while the EU AI Act applies a risk-based framework to qualifying medical devices. Potential bias can arise from unrepresentative study populations, nonrepresentative clinical language, proxy variables, aggregation and selective feature use.

Clinical trial populations can underrepresent women and non-White patients, and symptom documentation may also differ across racial, ethnic, gender and socioeconomic groups. AI-generated summaries can intensify this problem when they present a narrow or skewed dataset as representative of a whole population. The review highlights equity-centered frameworks that require risk-benefit assessment, formal oversight and mechanisms for redress, alongside proposals to add explicability to established bioethical principles.

Its overall assessment is that AI and PRO systems could make neuro-oncology more patient-centered and data-driven, but current evidence is constrained by small studies, missing symptom data, inconsistent measurement and weak external validation. Progress depends on multicenter collaboration, standardized PRO frameworks, interoperable data systems, reproducible validation and broader inputs such as caregiver reports and wearables. Translation into care will require interpretable, electronic-health-record-integrated tools that provide actionable information while preserving patient consent, privacy, autonomy, equity and clinician oversight.

Subject of Research: Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology

Article Title: Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology

Article References: Yogendran, L. V., Avery, C., Crooms, R. C., Ozair, A., Siegel, C., Guterman, I., Rhee, J. Y., Binder, J., Minor, M., Alder, L., Davis, J., & Sharma, A. (2026). Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology. Journal of Neuro-Oncology, 180(2), Article 26. https://doi.org/10.1007/s11060-026-05832-9

Image Credits: AI Generated

DOI: 10.1007/s11060-026-05832-9

Keywords: artificial intelligence, neuro-oncology, brain tumors, patient-reported outcomes, symptom monitoring, machine learning, large language models, clinical decision support, medical ethics, data bias, Artificial, intelligence