## 4 Discussion
However, individual interviews are useful in improving the final outcome by eliciting detailed responses through in-depth questioning. In addition, in the proposed cascading Delphi method, all experts took part in all stages of the process, including reviewing and editing their opinions and comments at each stage. The traditional version of data collection for FMEA had three main drawbacks: group bias, time consumption, and sample size issues [38; 39]. Snowball sampling is a suitable method for collecting data for research when experts are difficult to find. Snowball sampling creates a network of participants by finding one or two participants and then asking them to refer others. This method is suitable for participants with specific knowledge and experience when it is difficult to locate or contact experts on the topic. There does not appear to be a standard for the normal or minimum number of experts, but we believe that this cohort represents the essential disciplines and stakeholders, including designers, engineers, and users. Establishing a team of FMEA experts is an important step, as FMEA is a subjective method that requires input from experts. Experts should be selected to include stakeholders who are involved as both producers and users with direct experience with the equipment or device to be studied, and from a variety of professions to provide a comprehensive perspective. The opinions of the experts should then be diverse, like a puzzle that fits together into a single picture. Risk priority number (RPN) score is inherently often leading to variations due to differing opinions among teams. The exploration of Multi-Criteria Decision-Making (MCDM) techniques offers a significant advancement in improving reliability and overcoming the limitations of traditional RPN calculations. Conventionally, RPN scores in FMEA have relied on subjective assessments [7], which can lead to inconsistencies due to differing opinions among evaluators [21]. In contrast, MCDM techniques transform this process into a more objective methodology. By systematically integrating multiple criteria and considering the relationships and priorities among them, MCDM enhances the precision and consistency of the RPN assessment. This approach not only addresses the issue of equal weighting for severity, occurrence, and detection but also mitigates the variability arising from individual judgments. The adoption of MCDM techniques thus represents a promising step toward a more robust and reliable risk prioritization framework in FMEA. Hybrid MCDM approaches, such as the combination of TOPSIS and GRA, are widely employed to address the limitations of the traditional RPN by generating additional priority indices and weighting criteria [12]. These methodologies enhance the analysis by considering a broader range of elements, effectively tackling key weaknesses of conventional approaches. However, it is important to note that MCDM-based FMEA approaches often remain context dependent and may lack robustness in situations characterized by incomplete information, particularly during the design phase of FMEA [12]. Using generative AI in the content analysis can facilitate the process and lead to highlighted risk areas requiring mitigation for a flexible fetoscope. In addition, utilizing generative AI as a copilot has the potential to reduce the time required by more than half and reduce the risk of occupational back pain. There are several concerns in this study. First, the drop from 100% to 82% participation rate could reduce the diversity of expert opinions, introduce bias, weaken consensus, and affect the validity and consistency of final risk prioritization. These impacts are particularly significant given the small expert sample size. In this study, the limited sample size is due to the confidential nature of early fetoscope design and the need for reliable input from experts with direct, specialized knowledge and experiences. Second, delays of up to seven months in the Stage 3 response could skew the context of earlier discussions.