Background: Breast cancer is the most common form of cancer in Japan; genetic background and hereditary breast and ovarian cancer (HBOC) are implicated. The key to HBOC diagnosis involves screening to identify high-risk individuals. However, genetic medicine is still developing; thus, many patients who may potentially benefit from genetic medicine have not yet been identified. Objective: This study’s objective is to develop a chatbot system that uses augmented intelligence for HBOC screening to determine whether patients meet the National Comprehensive Cancer Network (NCCN) BRCA1/2 testing criteria. Methods: The system was evaluated by a doctor specializing in genetic medicine and certified genetic counselors. We prepared 3 scenarios and created a conversation with the chatbot to reflect each one. Then we evaluated chatbot feasibility, the required time, the medical accuracy of conversations and family history, and the final result. Results: The times required for the conversation were 7 minutes for scenario 1, 15 minutes for scenario 2, and 16 minutes for scenario 3. Scenarios 1 and 2 met the BRCA1/2 testing criteria, but scenario 3 did not, and this result was consistent with the findings of 3 experts who retrospectively reviewed conversations with the chatbot according to the 3 scenarios. A family history comparison ascertained by the chatbot with the actual scenarios revealed that each result was consistent with each scenario. From a genetic medicine perspective, no errors were noted by the 3 experts. Conclusions: This study demonstrated that chatbot systems could be applied to preliminary genetic medicine screening for HBOC.

Background: Hereditary breast and ovarian cancer (HBOC) is a major type of hereditary cancer. Establishing effective screening to identify high-risk individuals for HBOC remains a challenge. We developed a prototype of a chatbot system that uses artificial intelligence (AI) for preliminary HBOC screening to determine whether individuals meet the National Comprehensive Cancer Network BRCA1/2 testing criteria. Objective: This study’s objective was to validate the feasibility of this chatbot in a clinical setting by using it on a patient population that visited a hospital. Methods: We validated the medical accuracy of the chatbot system by performing a test on patients who consecutively visited the Kanagawa Cancer Center. The participants completed a preoperation questionnaire to understand their background, including information technology literacy. After the operation, qualitative interviews were conducted to collect data on the usability and acceptability of the system and examine points needing improvement. Results: A total of 11 participants were enrolled between October and December 2020. All of the participants were women, and among them, 10 (91%) had cancer. According to the questionnaire, 6 (54%) participants had never heard of a chatbot, while 7 (64%) had never used one. All participants were able to complete the chatbot operation, and the average time required for the operation was 18.0 (SD 5.44) minutes. The determinations by the chatbot of whether the participants met the BRCA1/2 testing criteria based on their medical and family history were consistent with those by certified genetic counselors (CGCs). We compared the medical histories obtained from the participants by the CGCs with those by the chatbot. Of the 11 participants, 3 (27%) entered information different from that obtained by the CGCs. These discrepancies were caused by the participant’s omissions or communication errors with the chatbot. Regarding the family histories, the chatbot provided new information for 3 (27%) of the 11 participants and complemented information for the family members of 5 (45%) participants not interviewed by the CGCs. The chatbot could not obtain some information on the family history of 6 (54%) participants due to several reasons, such as being outside of the scope of the chatbot’s interview questions, the participant’s omissions, and communication errors with the chatbot. Interview data were classified into the following: (1) features, (2) appearance, (3) usability and preferences, (4) concerns, (5) benefits, and (6) implementation. Favorable comments on implementation feasibility and comments on improvements were also obtained. Conclusions: This study demonstrated that the preliminary screening system for HBOC using an AI chatbot was feasible for real patients.

Clinical screening using the National Comprehensive Cancer Network (NCCN) testing criteria may fail to identify all patients with hereditary breast and ovarian cancers. Thus, this study aimed to evaluate the strategy of expanding target patients for genetic testing among Japanese patients. We reviewed the medical records of 91 breast cancer patients who underwent genetic testing. Among 91 patients, eight were diagnosed with pathogenic or likely pathogenic variants: BRCA1 (n = 4) and BRCA2 (n = 4). Among 50 patients meeting the testing criteria of the guidelines, 6 (12%) were diagnosed with pathogenic or likely pathogenic variants. The sensitivity and specificity of screening using the testing criteria were 75% and 47%, respectively. Expanding the NCCN criteria to include all women diagnosed with breast cancer aged ≤65 years achieved 88% sensitivity but 8% specificity. The expansion of the NCCN criteria could benefit Japanese patients; however, larger studies are necessary to change clinical practice.

In Japan, insurance coverage for BRCA genetic testing was expanded in 2020. The reasons individuals undergo or decline testing remain unclear; however, cost has been identified as a key factor. Herein, we investigated barriers and motivators for undergoing testing. The subjects were 1025 individuals who received genetic counseling at our institution between April 2014 and March 2024. We included 647 patients and 82 blood relatives. We compared the number of patients undergoing tests before and after insurance coverage to evaluate costs. Patients were classified as tested or not tested. Clinical backgrounds and reasons for undergoing or declining testing were compared retrospectively using medical and counseling records. We found that 485 patients underwent testing. There was a significant increase in the number of patients who underwent testing in the 4 years after coverage ( n = 376) compared with the 6 years before insurance coverage ( n = 109; p < 0.001). Furthermore, there were significant differences in insurance/self‐pay, treatment status (preoperative/during treatment), and age at first counseling. The main reasons for declining testing were “Discuss options with relatives” (28%) and “Focused on current treatment” (14.3%). Cost‐related concerns markedly decreased after insurance coverage, suggesting cost had been an important structural barrier. Motivators for testing included “Determine the surgical technique” (42.7%) and “For my health care” (22.3%). The blood relative testing rate was high at 91% (75/82). In addition to our finding that cost had been an important barrier to testing, we identified several other non‐medical barriers. In Japan, the selection of candidates for testing and clinical indications are determined according to Japanese HBOC clinical guidelines. We identified determination of the surgical technique as the main motivator, as well as the timing of counseling being important. Understanding barriers and motivators could help clients who need testing.

BACKGROUND Hereditary breast and ovarian cancer (HBOC) is a major type of hereditary cancer. Establishing effective screening to identify high-risk individuals for HBOC remains a challenge. We developed a prototype of a chatbot system that uses artificial intelligence (AI) for preliminary HBOC screening to determine whether individuals meet the National Comprehensive Cancer Network BRCA1/2 testing criteria. OBJECTIVE This study’s objective was to validate the feasibility of this chatbot in a clinical setting by using it on a patient population that visited a hospital. METHODS We validated the medical accuracy of the chatbot system by performing a test on patients who consecutively visited the Kanagawa Cancer Center. The participants completed a preoperation questionnaire to understand their background, including information technology literacy. After the operation, qualitative interviews were conducted to collect data on the usability and acceptability of the system and examine points needing improvement. RESULTS A total of 11 participants were enrolled between October and December 2020. All of the participants were women, and among them, 10 (91%) had cancer. According to the questionnaire, 6 (54%) participants had never heard of a chatbot, while 7 (64%) had never used one. All participants were able to complete the chatbot operation, and the average time required for the operation was 18.0 (SD 5.44) minutes. The determinations by the chatbot of whether the participants met the BRCA1/2 testing criteria based on their medical and family history were consistent with those by certified genetic counselors (CGCs). We compared the medical histories obtained from the participants by the CGCs with those by the chatbot. Of the 11 participants, 3 (27%) entered information different from that obtained by the CGCs. These discrepancies were caused by the participant’s omissions or communication errors with the chatbot. Regarding the family histories, the chatbot provided new information for 3 (27%) of the 11 participants and complemented information for the family members of 5 (45%) participants not interviewed by the CGCs. The chatbot could not obtain some information on the family history of 6 (54%) participants due to several reasons, such as being outside of the scope of the chatbot’s interview questions, the participant’s omissions, and communication errors with the chatbot. Interview data were classified into the following: (1) features, (2) appearance, (3) usability and preferences, (4) concerns, (5) benefits, and (6) implementation. Favorable comments on implementation feasibility and comments on improvements were also obtained. CONCLUSIONS This study demonstrated that the preliminary screening system for HBOC using an AI chatbot was feasible for real patients.

BACKGROUND Breast cancer is the most common form of cancer in Japan; genetic background and hereditary breast and ovarian cancer (HBOC) are implicated. The key to HBOC diagnosis involves screening to identify high-risk individuals. However, genetic medicine is still developing; thus, many patients who may potentially benefit from genetic medicine have not yet been identified. OBJECTIVE This study’s objective is to develop a chatbot system that uses augmented intelligence for HBOC screening to determine whether patients meet the National Comprehensive Cancer Network (NCCN) BRCA1/2 testing criteria. METHODS The system was evaluated by a doctor specializing in genetic medicine and certified genetic counselors. We prepared 3 scenarios and created a conversation with the chatbot to reflect each one. Then we evaluated chatbot feasibility, the required time, the medical accuracy of conversations and family history, and the final result. RESULTS The times required for the conversation were 7 minutes for scenario 1, 15 minutes for scenario 2, and 16 minutes for scenario 3. Scenarios 1 and 2 met the BRCA1/2 testing criteria, but scenario 3 did not, and this result was consistent with the findings of 3 experts who retrospectively reviewed conversations with the chatbot according to the 3 scenarios. A family history comparison ascertained by the chatbot with the actual scenarios revealed that each result was consistent with each scenario. From a genetic medicine perspective, no errors were noted by the 3 experts. CONCLUSIONS This study demonstrated that chatbot systems could be applied to preliminary genetic medicine screening for HBOC.