To ensure the clinical practicality and national applicability of the system, we invited senior gynecologic oncologists, genetic counselors, clinical research nurses, and patient representatives to participate in several online and offline seminars.

This study was a technological innovation and validation research that used participatory design and agile iterative development methods (Figure 1). The research process strictly adhered to ethical guidelines, with all involved patient data anonymized and the study approved by the relevant ethics committee.

As the first step in building the system, multiple experienced genetic counselors collaborated with AI engineers to develop initial scripts for the system’s knowledge base and dialogue flows. To ensure the professionalism and accuracy of medical information, every guided question was reviewed by clinical genetics experts. Meanwhile, medical education specialists also participated to ensure that the dialogue language remained accessible and avoided potential misunderstandings caused by technical jargon.

When a user (patient) mentioned disease information about relatives (eg, “my mother’s sister was diagnosed with ovarian cancer at age 50”) during dialogue with the agent, the system’s back end natural language processing engine and specialized LLM worked collaboratively to perform entity recognition (identifying kinship relationships, diseases, and ages) and relationship extraction. Subsequently, using proprietary optimization algorithms, the system further enhanced the accuracy of relationship entities, automatically constructed a visualized pedigree chart according to genetic standards, and rendered it in real time.

We developed a mobile app that served as a user interface for both physicians and patients. The entire system was based on a microservices architecture, which ensured secure and efficient communication between the physician and the patient, as well as between the front end and back end natural language processors. To protect user privacy, all sensitive information was encrypted before data transmission, and conversation content was processed and stored anonymously, accessible only to authorized administrators for system optimization.

The system consisted of a base large model, 2 core agents, 3 support platforms, and 2 types of terminal apps. The base model was the self-developed multimodal medical model. The core agents were the family acquisition agent and the risk computation agent. The system provided services through 2 entrances: the physician side (web) and the patient side (app).

The iterative process was initially planned to include 3 iterations, but during the development process, to address the accuracy of AI models when dealing with complex kinships, we decided to add a fourth iteration dedicated to manually reviewing and optimizing the genealogy map generation algorithm. Each iteration signified a significant expansion of the database and substantial improvements in system functionality.

We manually evaluated all conversations generated during the user testing and the generated family charts to assess system performance, categorizing the results into 3 categories: correct (accurate information extraction and correct family chart structure), wrong (missing key information or mismatched relationships), or fallback (the system could not understand the user’s answer and asked the user to phrase it in a different way).

All data used for tool iteration were derived from real-world, multiturn dialogue records generated by users during system use. At the initial stage of tool development, multiple experienced genetic counselors, clinical geneticists, and medical education specialists jointly reviewed the dialogue flow scripts, based on which the development team finalized requirements and established preliminary process rules. During iteration, the rules were continuously refined using real-world conversation data validated by experts; by contrast, dialogue data collected at user testing checkpoints served as an independent test set, and user feedback was systematically incorporated into the rule revision loop to ensure the objectivity and clinical relevance of improvements. The gold standard was jointly established by a panel of industry experts. Annotation, including entity recognition and attribute labeling of pedigree data, was primarily performed by relevant project personnel and 1 clinical staff member. All annotation disagreements were resolved through multiple offline discussions until consensus was reached, and the entire annotation process was strictly independent of the iterative optimization data to guarantee the reliability of the evaluation results.

The agent was exclusively designed for gynecological oncology medical knowledge consultation, family history collection, and pedigree chart generation and did not output diagnostic or therapeutic decisions. The RAG system was restricted to a defined knowledge source, and all results were accompanied by source attribution. Knowledge extraction incorporated confidence filtering, rule-based validation, and human review; newly added knowledge must not conflict with existing high-confidence knowledge. Both the generated pedigree chart and the rules participated in result verification. These measures collectively supported the claim that the agent was subject to multilayered constraints and did not engage in free-form generation.

In the process of extracting family tumor history information and generating the pedigree chart, a mechanism of relationship confidence control and kinship consistency verification was introduced. For recognition results with low confidence or conflicting kinship relations, the agent did not directly incorporate them into the pedigree chart but instead triggered targeted follow-up questions. If the uncertainty persisted, a conservative fallback strategy was adopted, marking the relevant nodes or relationships as “unknown” or “pending confirmation.”

The generated pedigree chart was further validated against predefined kinship rules, including branch affiliation (paternal vs maternal), generational level, intragenerational and intergenerational relationships, key connecting kinships, and consistency checks for duplicate members. This approach thereby enhanced the logical consistency and traceability of the structured family tumor history results.

The present study involving clinical hereditary cancer information from patients was approved by the ethics committee of the Obstetrics and Gynecology Hospital of Fudan University (2021‐09-X1). Prior informed consent was obtained from all users, who were required to agree to relevant protocols and policies before participation; all collected data were deidentified prior to use to ensure that no personally identifiable information was involved.

In this study, an intelligent assisted diagnostic decision-making system designed for hereditary gynecological tumors was successfully constructed and preliminarily verified. The final pilot version achieved near-perfect accuracy in generating genealogy nodes and relationships. Although AI functions were initially limited, the final system received positive feedback from test users regarding its innovation, ease of use, and intuitive information presentation. The developed system was an accurate, efficient auxiliary tool. After sufficient clinical validation, it holds promise for integration into routine clinical workflows to optimize genetic counseling services. The entire development process was more challenging than expected, but it turned out that AI tools have the potential to become a powerful complement to traditional genetic counseling models by combining large specialized models with clinical workflows [10,11].

The core strength of this project was its specialization and participatory design. Unlike tools that rely on general large models, the specialized large model we used effectively reduced the risk of generating clinically inaccurate information through knowledge-base construction in the field of gynecological oncology. The participatory design approach used throughout ensured that the functions, interactions, and outputs of the system (eg, the style of the family map) were closely aligned with the actual work habits and needs of clinicians, which is the key to the successful implementation of the technology in clinical practice. It is worth noting that the core information nodes of the database expanded from the initial 100 to the final 245, while the number of user expression examples recognizable by the system increased from 800 to 2256 (the recognition breadth ratio increased from 8.0 to 9.2), reflecting the great efforts we made to improve the AI’s understanding capabilities and the accuracy of genealogy map generation during the development process.

The biggest challenge in the development process, consistent with the literature, was the precise translation of highly unstructured, ambiguous natural language conversations into structured data and knowledge graphs (ie, family maps) that complied with medical genetics norms. Our experience shows that the general capabilities of LLMs alone are not enough to solve this problem. The introduction of the graph-based kinship inference and validation module and the implementation of multiple rounds of data-driven iterative optimization are effective ways to overcome this challenge. The significant increase in the scale of the system’s database intuitively reflects the great efforts made to improve AI understanding capabilities.

The clinical significance of this system is far-reaching. By allowing patients to interact with the system before consultation, family history collection, the longest and most error-prone task in the traditional mode can be efficiently completed. This allows genetic counselors to free valuable clinic time from tedious data entry and mapping work and focus more on in-depth communication with patients, interpreting complex genetic reports, and providing psychological support, thereby greatly improving the quality and accessibility of genetic counseling.

The value of this informatics approach is not limited to accuracy metrics. In routine clinical practice, shifting family history collection, the longest and most error-prone task, to a self-service interaction before the consultation frees counselors from manual charting and lets them spend the saved time on interpretation, risk communication, and psychological support; standardized capture also reduces between-center variation. On the personalized-care side, pairing the gynecology-oncology RAG knowledge base with rule-based risk logic and the graph-based kinship inference module converts unstructured family narratives into individual-specific pedigrees and risk cues, so assessment can follow the patient rather than a generic template. From a population health policy perspective, taking the tool into primary care and community settings would broaden access to pedigree-based screening for high-risk families, supply the data needed for cost-effectiveness analyses, and help shape standardized family history protocols that extend hereditary cancer prevention to the population level.

Although the development of the system involved the participation of multicenter experts across the country, its clinical validation was mainly carried out at a single medical center with a small sample size. Larger-scale, multicenter prospective studies are needed in the future to verify its effectiveness and generalization ability in different clinical settings. During the design phase, despite the full involvement of patient representatives, it would have been better to involve a more diverse patient population in each round of testing. We have initiated a follow-up study to conduct in-depth interviews with patients using the system in a real-world clinical setting, the results of which will be used for the final iteration of the system.

While certain pedigree resources were consulted during the development of the tool, its functionality relies on active user input. The primary focus of this paper is the conversion of natural language into structured pedigree representation; benchmarking against existing pedigree software or other AI tools has not yet been conducted. Currently, most existing pedigree drawing software relies heavily on manual data entry and structured form-filling, lacking the capability to process unstructured, multiturn natural language dialogues. Therefore, conducting a direct, quantitative head-to-head comparison is challenging, as the input modalities are fundamentally different. However, we recognize the importance of this comparison, and such comparisons have been considered for inclusion in subsequent testing.

Successful implementation of AI tools in health care requires systems that can accurately analyze user inputs and provide correct and reliable outputs. As AI technology evolves to better understand the nuances of written language on a deeper level, patients will be able to fully understand and prepare information through interaction with the intelligent system before meeting with the physician. This not only makes physician-patient communication more efficient and targeted but also makes personalized medicine truly a reality. Tools such as the hereditary gynecological tumor assistance system are expected to become efficient tools for genetic counselors.