ArticleNPJ digital medicine2025
When investigator meets large language models: a qualitative analysis of cancer patient decision-making journeys.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Beyond one-size-fits-all: mapping information-seeking and decision-making pathways in cancer care.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Generative Artificial Intelligence for Qualitative Methods in Health Research: Rapid Review.Journal of medical Internet research · 2026Review
- Exploring Attitudes of Primary Caregivers Towards Pediatric Tissue-Based Research using Large Language Models: Insights from Rural and Urban Community Calls and Surveys.medRxiv : the preprint server for health sciences · 2026Article
- "Do It by Myself" or Autonomy, Participation, and Assistive Devices and Technology Needs of Children and Youth With Disabilities: Text Mining Analysis of a National Survey in France.JMIR medical informatics · 2026Article
- Attitudes and Willingness to Participate in Drug Clinical Trials Among Patients With Cancer: Multistage Qualitative Study.Journal of medical Internet research · 2026Article
- Article
- Artificial Intelligence-Enhanced Molecular Profiling of JAK-STAT Pathway Alterations in FOLFOX-Treated Early-Onset Colorectal Cancer.International journal of molecular sciences · 2026Article
- Parent Perspectives on Dietary and Nutraceutical Therapies in Autism Spectrum Disorder: A Qualitative Thematic Analysis Using a Large Language Model.Journal of restorative medicine · 2026Article
- Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research.Research square · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are transforming the landscape of healthcare research, yet their role in qualitative analysis remains underexplored. This study compares human-led and LLM-assisted approaches to analyzing cancer patient narratives, using 33 semi-structured interviews. We conducted three parallel analyses: investigator-led thematic analysis, ChatGPT-4o, and Gemini Advance Pro 1.5. The investigator-led approach identified psychosocial and emotional themes, while the LLMs highlighted structural, temporal, and logistical aspects. LLMs demonstrated efficiency in identifying recurring patterns but struggled with emotional nuance and contextual depth. Investigator-led analysis, while time-intensive, captured the complexities of identity disruption and emotional processing. Our findings suggest that LLMs can serve as complementary tools in qualitative research, enhancing analytical breadth when paired with human interpretation. This study proposes a hybrid model integrating AI-assisted and human-led methods and offers practical recommendations for responsibly incorporating LLMs into qualitative health research.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.