## 4 Discussion
One way to address the weaknesses identified in this study is by ensuring that the facilitator must have in-depth knowledge and understanding of the equipment's operation to be studied to be able to analyze the data more accurately. The editing process, which often involves multiple iterations between experts and facilitator, is a process which carries an intrinsic risk of facilitator bias, of which the facilitator must be conscious. Being aware of this risk is essential. The facilitator must be patient and have task-tracking skills. The facilitator must use both art and science in tracking data to maximize cooperation. Multiple communication channels will help in tracking data, especially face-to-face tracking. Good relationships between the research team and experts are very important in obtaining quality data and lowering the dropout rate. Since the device to be studied is still in the design phase, it is vital that as much information as possible, including detailed drawings, mock-ups, and samples of componentswhich may already exist, should be provided to the experts, either physically, or in the form of images or video clips. We found that experts often confused possible failure modes with possible causes of failure, so it is important to clarify this. Cost and environmental factors should be considered to cover the production of medical devices. The cascading Delphi method-based FMEA framework, developed in alignment with ISO 14971, addresses risk management challenges for medical devices in the early design phase and applies to a variety of medical devices. The proposed framework could be applied to devices such as new endoscopes or minimal invasive surgical tools. For example, in patient's safety aspect, similar failure causes and effects related to device's components could be assessed using the same process. Our proposed framework was previously piloted using an electromagnetic self-tracking fetoscope before being refined and validated with a flexible fetoscope, allowing us to address how key steps can be generalized or adapted for other applications. While the hybrid MCDM method applied in this study offers a structured and quantitative approach to prioritizing risk, alternative methods such as decision tree models integrated with fuzzy logic may provide additional flexibility and interpretability. These models are particularly useful when expert assessments are qualitative or expressed in linguistic terms. Future work may explore such methods to handle uncertainty in early-stage design even more effectively, allowing rule-based logic and fuzzy inference to complement the numerical analysis of risk. Future research could add steps to reach consensus by sending expert reports for review both individually and as a group in each stage, but fatigue and impatience of experts must be taken into account. The analysis of qualitative data in Stage 3 was time consuming. Using machine learning or artificial intelligence tools can reduce the workload and save time; however, the facilitator must still collect data to obtain consistent, systematic, and verifiable qualitative data and scores. This study involved only risk identification, risk analysis, and evaluating which risk scores are calculated and prioritized for potential FCs. High-risk FCs need to be analyzed by experts to provide corrective actions to mitigate or eliminate risks. Finally, the risk is re-evaluated after redesigning to verify that the interventions have effectively lowered it to an acceptable level.