ArticleBrain & spine2026
Artificial intelligence chatbots in response to patient's common inquiries about chordoma: A cross-sectional study.
Article in Brain & spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial intelligence (AI) chatbots has been well studied in many common diseases. However, little was reported for chordoma, which is a rare disease with high rates of recurrence, disability, and mortality. Research question: This study aims to assess the performance of oncologists and state-of-the-art AI chatbots in response to frequently-asked questions (FAQs) of chordoma from real world. The response performance was mainly evaluated by overall quality, empathy, and readability. Material and methods: Sixty chordoma-related FAQs, collected from social media, were addressed by various chatbots and oncologists, and the best-performing chatbot-generated text was further edited and assessed again. Rated scores were ordered for quality and empathy in a blind way. The readability was measured objectively by calculating Flesch-Kincaid Grade Level (FKGL), Automated Readability Index (ARI), and Gunning-Fog Index (GFI). Results: AI chatbots were universally superior to oncologists in response quality (3.86 ± 0.14 vs. 3.12 ± 0.25, p < 0.001) and empathy (3.28 ± 0.41 vs. 2.95 ± 0.48, p < 0.001). DeepSeek-R1 achieved highest rated score in response quality (4.20 ± 0.22), while Claude 3.5 Sonnet was considered as the best chatbots by comprehensive assessments. The chatbot drafted responses were easier to understand from patient's perspective (p < 0.001). Improved response quality (4.09 ± 0.12, p < 0.001), empathy (4.00 ± 0.39, p < 0.001), and readability (FKGL: 11.30 ± 2.42, p < 0.001) were obtained after editing the Claude-3.5-generated responses by oncologists. Discussion and conclusions: AI chatbots reached favorable quality and empathetic performance in response to chordoma-related FAQs, and generated equivalent readability compared to oncologists. With generative chatbot's assistance, oncologists may response more comprehensively and efficiently in addressing chordoma patient's common inquiries.
Indexed as
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.