Evidence map›Paper›PMID 40802992›Full record

ArticleJMIR cancer2025

Evaluating a Chatbot as a Companion for Patients With Breast Cancer: Collaborative Pilot Study.

Sebastian Daniel Boie, Esther Glastetter, Michael Patrick Lux, Felix Balzer, Christof von Kalle, Christian Lenz, Ulrike Müller

Abstract read
In one paragraph

Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. [The added value of self-help-oriented research in oncology for stakeholders in Germany].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026
    Review
  6. Review
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sebastian Daniel BoiePfizer Pharma GmbH, Friedrichstr. 110, Berlin, 10117, Germany, 49 15152377580.ORCID 0000-0001-7993-7901
Esther GlastetterPfizer Pharma GmbH, Friedrichstr. 110, Berlin, 10117, Germany, 49 15152377580.ORCID 0009-0001-9987-4319
Michael Patrick LuxDepartment for Gynecology and Obstetrics, St. Louise Women's Hospital, Paderborn, St. Josefs Women's Hospital, Salzkotten, St. Vincenz Clinics, Salzkotten + Paderborn, Germany.ORCID 0000-0002-2781-2178
Felix BalzerInstitute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0003-1575-2056
Christof von KalleClinical Study Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0001-9221-3297
Christian LenzPfizer Pharma GmbH, Friedrichstr. 110, Berlin, 10117, Germany, 49 15152377580.ORCID 0009-0001-0863-2219
Ulrike MüllerPfizer Pharma GmbH, Friedrichstr. 110, Berlin, 10117, Germany, 49 15152377580.ORCID 0009-0006-4255-9664

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients with breast cancer frequently experience significant uncertainty, prompting them to seek detailed, personalized, and reliable medical information to enhance adherence to prescribed treatments, medications, and recommended lifestyle adjustments. Although high-quality information exists within oncology guidelines and patient-oriented resources, the provision of tailored responses to individual patient queries remains challenging, especially for non-English-speaking populations. Objective: This study aims to evaluate the potential of an artificial intelligence-driven chatbot, specifically leveraging ChatGPT (GPT-4; OpenAI) combined with retrieval-augmented generation, to deliver personalized answers to complex breast cancer-related patient questions in German. Methods: We collaborated with one of Germany's largest breast cancer Patient Representation Groups to collect authentic patient inquiries, receiving a total of 118 questions. After initial screening, we selected 104 medical questions, organized into 7 distinct categories: aftercare, bone health, ductal carcinoma in situ, diagnostics, nutrition and supplements, complementary medicine, and therapy. A customized version of GPT-4 was configured with specific system prompts emphasizing empathetic, evidence-based responses and integrated with a comprehensive database comprising guidelines, recommendations, and patient information materials published by recognized German medical societies. To assess chatbot responses, we used 4 evaluation criteria: comprehensibility (clarity from a patient perspective), correctness (accuracy per current medical guidelines), completeness (inclusion of all relevant aspects), and potential harm (risk of undue patient harm or misinformation). Ratings were conducted using a 5-point Likert scale by a breast cancer expert (correctness, completeness, and potential harm) and patient representatives (comprehensibility). Results: The chatbot provided high-quality responses across multiple dimensions. Of the 499 responses evaluated for comprehensibility, 427 (85.6%) were rated as comprehensible. Among the 104 responses assessed for the remaining dimensions, 91 (87.5%) were rated as correct, 72 (69.2%) as complete, and 93 (89.4%) as nonharmful. Reasons for incomplete answers included omission of reimbursement details, updates from recent therapeutic guidelines, or nuanced recommendations regarding endocrine therapy and aftercare schedules. In addition, 6 (5.8%) of the answers were rated as potentially harmful due to outdated or contextually inappropriate recommendations. The chatbot also performed well in the nutrition and bone health categories despite occasionally incomplete document retrieval. Conclusions: Our findings demonstrate that an artificial intelligence-powered chatbot with GPT-4 and retrieval augmentation can effectively provide personalized, linguistically accessible, and largely accurate information to German-speaking patients with breast cancer. This approach holds considerable promise for improving patient-centered communication, empowering patients to make informed decisions. Nonetheless, observed limitations regarding response completeness and potential harm underscore the critical need for ongoing human oversight. Future research and development should prioritize regularly updated databases, advanced retrieval methods to handle complex document structures, multimodal capabilities, and clearly articulated disclaimers emphasizing the necessity of professional medical consultation. Our evaluation, along with the provided set of realistic patient questions, establishes a benchmark for future development and validation of German-language oncology chatbots.

Indexed as

Artificial IntelligenceBreast NeoplasmsFemaleGenerative Artificial IntelligenceGermanyHumansMiddle AgedPilot Projectsbreast cancerchatbotGen AIGenerative artificial intelligencepatient informationRAGRetrieval-augmented generation

Identifiers

PMID40802992
PMCPMC12373300

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.