Evaluation of the Rosa Chatbot Providing Genetic Information to Patients at Risk of Hereditary Breast and Ovarian Cancer: Qualitative Interview Study
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Abstract
Background: Genetic testing has become an integrated part of health care for patients with breast or ovarian cancer, and the increasing demand for genetic testing is accompanied by an increasing need for easy access to reliable genetic information for patients. Therefore, we developed a chatbot app (Rosa) that is able to perform humanlike digital conversations about genetic BRCA testing. Objective: Before implementing this new information service in daily clinical practice, we wanted to explore 2 aspects of chatbot use: the perceived utility and trust in chatbot technology among healthy patients at risk of hereditary cancer and how interaction with a chatbot regarding sensitive information about hereditary cancer influences patients. Methods: Overall, 175 healthy individuals at risk of hereditary breast and ovarian cancer were invited to test the chatbot, Rosa, before and after genetic counseling. To secure a varied sample, participants were recruited from all cancer genetic clinics in Norway, and the selection was based on age, gender, and risk of having a BRCA pathogenic variant. Among the 34.9% (61/175) of participants who consented for individual interview, a selected subgroup (16/61, 26%) shared their experience through in-depth interviews via video. The semistructured interviews covered the following topics: usability, perceived usefulness, trust in the information received via the chatbot, how Rosa influenced the user, and thoughts about future use of digital tools in health care. The transcripts were analyzed using the stepwise-deductive inductive approach. Results: The overall finding was that the chatbot was very welcomed by the participants. They appreciated the 24/7 availability wherever they were and the possibility to use it to prepare for genetic counseling and to repeat and ask questions about what had been said afterward. As Rosa was created by health care professionals, they also valued the information they received as being medically correct. Rosa was referred to as being better than Google because it provided specific and reliable answers to their questions. The findings were summed up in 3 concepts: “Anytime, anywhere”; “In addition, not instead”; and “Trustworthy and true.” All participants (16/16) denied increased worry after reading about genetic testing and hereditary breast and ovarian cancer in Rosa. Conclusions: Our results indicate that a genetic information chatbot has the potential to contribute to easy access to uniform information for patients at risk of hereditary breast and ovarian cancer, regardless of geographical location. The 24/7 availability of quality-assured information, tailored to the specific situation, had a reassuring effect on our participants. It was consistent across concepts that Rosa was a tool for preparation and repetition; however, none of the participants (0/16) supported that Rosa could replace genetic counseling if hereditary cancer was confirmed. This indicates that a chatbot can be a well-suited digital companion to genetic counseling.
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Patients were not completely satisfied with CAs due to lacking customisation.
“limitations such as insufficient customisation, lack of empathy, and defects in understanding free-input questions were noted.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Results (Emerging Concepts; page unavailable) report participants’ concerns about Rosa’s lack of empathy and its difficulty understanding self-composed questions. The Introduction (Background; page unavailable) says personalized communication was not included, but the paper does not clearly say patients were dissatisfied because of insufficient customization; the Discussion (Principal Findings) instead describes an overall positive attitude toward Rosa. (LLM, conf=0.83)
Experimental — accuracy not yet measuredPatients reported that CAs had an unstable understanding of self-written questions, failing to accurately provide the information they expected.
“During interaction with CAs, patients reported that CAs had an unstable understanding of self-written questions, failing to accurately provide the information they expected (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Results (Emerging Concepts; page not available) say several interviewees reported that Rosa inconsistently understood self-composed questions and switched to suggested readings. The Discussion (Principal Findings; page not available) reports a high fallback rate for open-ended questions, but does not establish that the information Rosa provided was inaccurate or consistently failed to meet patients’ expectations. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPeople at high risk for cancer felt comforted after acquiring cancer-related knowledge through CAs before genetic counselling.
“People at high risk for cancer felt comforted after acquiring cancer-related knowledge through CAs before genetic counselling (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
In Results > Emerging Concepts, one participant who used Rosa before counseling said its information helped her feel calm and made the situation less difficult. Results > Participant Characteristics says only 1 of 16 participants downloaded Rosa before seeing the counselor; others described wanting earlier access or finding Rosa comforting alongside counseling. The claim overstates the evidence for people feeling comforted specifically after using a chatbot before counseling. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFive studies found patients had few barriers to interacting with CAs.
“Five studies found patients had few barriers to interacting with CAs (Goertz et al. 2023;Qiu et al. 2021;Siglen et al. 2023;Tawfik et al. 2023;Visvanathan et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Results (Emerging Concepts; page unavailable) describe easy access, helpful prompts, and positive engagement with Rosa, which support the idea that patients could interact with it. But 7 of 16 participants reported communication shortcomings, including difficulty getting answers to self-composed questions, and 8 of 16 noted the lack of human touch. Describing barriers as few omits these substantial limitations. (LLM, conf=0.83)
Experimental — accuracy not yet measuredPatients felt that CAs resembled talking to a nurse because their knowledge base was based on global consensus, expert guidelines, and professional knowledge rather than mixed internet information.
“Due to CAs' knowledge base being based on global consensus, expert guidelines, and professional knowledge, rather than the mixed information available on the internet, patients felt that it resembled talking to a nurse (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Results (Emerging Concepts; page not provided) say participants trusted Rosa because health care personnel wrote its information, and some felt its language resembled talking to a nurse or physician. The paper does not establish that Rosa’s knowledge base drew on global consensus or expert guidelines, or that patients made this comparison because internet information was mixed. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Both quantitative and qualitative studies indicated that CAs provided emotional support for cancer patients before talking to doctors face-to-face.
“Additionally, both quantitative (Chaix et al. 2019;Greer et al. 2019) and qualitative studies (Siglen et al. 2023) indicated that CAs provided emotional support for cancer patients before talking to doctors face-to-face.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Results (Emerging Concepts; page unavailable) describe a participant who felt calm before meeting a genetic counselor after using Rosa, and another who found the chatbot comforting. However, the study concerned people at risk of hereditary cancer, not necessarily patients with cancer, and it does not establish that the subsequent conversations were face-to-face with doctors. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSome knowledge bases of conversational agents were pre-programmed based on common cancer-related issues or professional guidelines.
“Specifically, some knowledge bases of CAs were pre-programmed based on common cancer-related issues or professional guidelines (Siglen et al. 2023)”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Introduction (Background; page unavailable) says Rosa has a database of predefined answers about hereditary breast and ovarian cancer written by genetic counselors and geneticists. This supports a pre-programmed, cancer-related knowledge base, but the paper does not say its answers were selected based on common issues or derived from professional guidelines. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA patient described the conversational agent as a friend who remained with her throughout her disease journey.
“A patient described the CAs as a friend who remained with her throughout her disease journey (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
The Discussion’s Principal Findings say a participant described Rosa as “having a friend beside them, one who is there for the whole ride.” The paper places that description in the genetic testing process, not a journey through a diagnosed disease. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPatients preferred interactions with multiple options or 'read more' functions.
“Three studies reported that patients preferred interactions with multiple options or 'read more' functions.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
Patients thought CAs lacked empathy and a human touch, which limited their ability to handle special and complex issues.
“Patients thought that CAs lacked empathy and a human touch, which limited their ability to handle special and complex issues (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
CAs facilitated communication with patients' families when patients had no idea how to explain the disease.
“Additionally, CAs not only benefited patients but also facilitated communication with patients' families when patients had no idea how to explain the disease (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
CAs were available 24/7 and integrated into daily routine, allowing users to get instant consultations via mobile devices anytime and anywhere.
“Moreover, CAs were available 24/7 (Siglen et al. 2023) and integrated into daily routine (Qiu et al. 2021), allowing users to get instant consultations via their mobile devices anytime and anywhere.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
People felt more confident to make a wise choice about their treatment after getting information in advance.
“However, people felt more confident to make a wise choice about their treatment after getting information in advance (Siglen et al. 2023;Soley et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
Siglen et al. found patients tended to use the 'read more' function to continue conversations with CAs.
“Siglen et al. found patients tended to use the 'read more' function to continue conversations with CAs (Siglen et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
Technical limitations identified by the review include a lack of customisation and weakness in natural language processing.
“Technical limitations identified by this review, including a lack of customisation and weakness in natural language processing (Qiu et al. 2021;Siglen et al. 2023)”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Siglen et al. 2023
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Validation of a guidelines-based digital tool to assess the need for germline cancer genetic testing
Russell, C. D., Daley, A., Arnem, D. R. V., Hila, A., Johnson, K. J., Davies, J. N., Cytron, H. S., Ready, K. J., Armstrong, C. M., Sylvester, M. E., et al. (2024) · Hereditary Cancer in Clinical Practice · doi:10.1186/s13053-024-00298-0 · Cited by 5 · see citing works
“…the tool and GCs that we observed, combined with prior research on feasibility and acceptability of genomics digital tools, 27, 33, 36–38 , 43– 45 supports An important limitation of this work is that patient histories were not entered by patients themselves, but instead by the research team.”
MethodBather, J. R., Goodman, M. S., Harris, A., Fiol, G. D., Hess, R., Wetter, D. W., Chavez‐Yenter, D., Zhong, L., Kaiser‐Jackson, L., Chambers, R. L., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-13495-4 · Cited by 5 · see citing works
Bosschaerts, K., Kashefi, A., Marez, L. D., Conradie, P. D., Hoecke, S. V., & Ongenae, F. (2025) · Digital Health · doi:10.1177/20552076251381747 · Cited by 5 · see citing works
“Drawing on practises from existing literature, 68,69 we conducted an in-depth semi-structured interview focusing on a selected set of system outputs: four single triggers, a combined trigger, a location-based trigger, and an extended chatting session.”
Wollney, E. N., Sims, S. M., Ricks‐Santi, L. J., Eddy, E., Wiesman, D., & Fisher, C. L. (2025) · BMC Public Health · doi:10.1186/s12889-025-23488-4 · Cited by 4 · see citing works
“Although factors such as feasibility and utility have been highlighted in studies among patients who have used chatbots [18], little is known about factors impacting the first step in the success of implemen-tation—women’s adoption of the genetic screening via a chatbot—a primary implementation…”
InfluentialUser testing of a previsit chatbot developed for population genomic screening and counseling
Morgan, K., Goehringer, J., Lindsey‐Mills, Z., Brangan, A., Kaur, J., Walters, N., Savage, S. K., Sturm, A. C., & Campbell‐Salome, G. (2025) · Journal of Genetic Counseling · doi:10.1002/jgc4.70090 · Cited by 3 · see citing works
“…interviews and focus groups have been conducted to evaluate patients' perceptions about chatbot use (Luca et al., 2023; Schmidlen et al., 2019; Siglen et al., 2023), while other studies have focused on evaluating chatbot feasibility and clinical outcomes (Heald et al., 2020; Ireland et al.,…”
Moultrie, R., Andrews, S. M., Williams, K. M., Kutsa, O., Hudnell, T., Brailsford, J., Aguilar, S. J., Savage, S., Biesecker, B. B., & Hunter, J. E. (2025) · Journal of Genetic Counseling · doi:10.1002/jgc4.70081 · Cited by 2 · see citing works
“Further, another chatbot, Rosa, was assessed in a qualitative interview study in Norway to test among individuals recruited from cancer genetic clinics at risk for hereditary breast and ovarian cancer (Siglen et al., 2023).”
Cascade Chatbot: A Scalable Approach to Family-Based Genetic Testing for Hereditary Cancer Syndromes
Rivera, L. B. D., Mitchell, L., Ahsan, M. D., Chandler, I. R., Epstein, E. S., Borsato, E. P., Allen, C. G., Kaphingst, K. A., Bradshaw, R. L., Fiol, G. D., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00321 · Cited by 2 · see citing works
van Lingen, M. N., van Till, S. A. L., Giesbertz, N. A. A., Beinema, T. C., Ausems, M. G. E. M., Klaassen, R., Cornel, M. C., van den Heuvel, L. M., & van Tintelen, J. P. (2024) · medRxiv · doi:10.1101/2024.11.27.24318108 · Cited by 2 · see citing works
Nguyen, O. T., Ahmad, A., & Wiegmann, D. A. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251357910 · Cited by 1 · see citing works
“Eight studies examined how the agent’s design influenced trust (Al-Anezi, 2024; Au et al., 2023; Li et al., 2023; Luetke Lanfer et al., 2023; Martin-Hammond et al., 2019; Mayer et al., 2024; Pramod & Patil, 2024; Siglen et al., 2023).”
Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology
Haghighat, R., Levi, S. R., & Frey, M. K. (2026) · Clinical Obstetrics & Gynecology · doi:10.1097/grf.0000000000000996 · Cited by 1 · see citing works
Yuen, J., Guzman, A. C. D. D., Courtney, E., & Ngeow, J. Y. Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99967-0.00307-0
Scalia, J. L., Laprise, J. K., Thrift, J. R., Farrell, C. L., & Sarasua, S. M. (2026) · JMIR AI · doi:10.2196/76400
“[15,16,22,26,43,46,47,49,50].”
InfluentialKim, M. A., Kim, M., Park, Y., Park, C., Lee, C., Hwang, K., & Oh, H. (2026) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2026.103202
Leveraging AI chatbots in supporting breast and ovarian cancer patients: A scoping review
Hui, V. C. C., Tian, L., Feng, X., Yuan, X., Yorke, J., & Lee, Y. J. (2026) · Digital Health · doi:10.1177/20552076261451390
“45”
InfluentialChiang, S.-B., Lin, F.-S., & Hou, J.-Y. (2026) · doi:10.2196/preprints.101278
Coleman, M. R., Underkofler, K. A., Thomas, M. H., Gupta, A., Panzer, A. A., Fitzgibbon, K. S., & Ring, K. L. (2026) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-26-0114
The impact of the rise of the chatbots: transforming genetics clinical delivery and research
Haga, S. B., & MacNeal, M. (2026) · Personalized Medicine · doi:10.1080/17410541.2026.2688147
Artificial intelligence revolution in breast cancer management: a comprehensive review
Shang, Y., Chen, T., Li, X., Li, J., & Yang, Q. (2026) · Molecular and Digital Medicine · doi:10.1016/j.mdmed.2026.100010
Liu, X., Yan, H., Wang, W., Xie, Y., Jiaying, L., Li, J., Wang, L., & Ning, L. (2026) · Digital Health · doi:10.1177/20552076261493264
Gacki‐Smith, J., (19819987), C. W., Duquette, D., Glorioso, M., Yevugah, B., Anderson, G., Muhammad, L. N., Dong, S., Friedewald, J., Smith, J. D., et al. (2026) · Kidney Medicine · doi:10.1016/j.xkme.2026.101578
Turnbull, M. L., Ivy, W. X., Oktavianus, J., & Wang, Y. (2026) · Patient Education and Counseling · doi:10.1016/j.pec.2026.110348
GENETIC COUNSELING WITH AI CHATBOTS, RISK BENEFIT ANALYSIS
Parıltay, E., Kubar, E., Tokaç, E. C. B., & Canbay, F. B. T. (2026) · Journal of Istanbul Faculty of Medicine / İstanbul Tıp Fakültesi Dergisi · doi:10.26650/iuitfd.1989296
- Authors
- Siglen, E., Vetti, H. H., Augestad, M., Steen, V. M., Lunde, Å., & Bjorvatn, C.
- Affiliations
- Haukeland University Hospital (NO)
- VID Specialized University (NO)
- University of Bergen (NO)
- Corresponding author
- Siglen, Elen
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-09-01
- Volume
- 25
- Pages
- e46571
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC10504626
- OpenAlex
- W4385059386
- Cited by
- 33
- References
- 32
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 10
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Haukeland Universitetssjukehus
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- chatbots, genetic counseling, BRCA testing, hereditary breast and ovarian cancer, genetic information, patient trust, perceived usefulness, usability, digital health tools, patient experience, in-depth interviews, qualitative interviews
- MeSH terms
- Female, Genetic Testing, Humans, Ovarian Neoplasms, Genetic Predisposition to Disease, Rosa, Qualitative Research
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