## 2 Problem Description  
Communication between doctors and patients has always been a critical focus in the medical field, particularly in pediatric consultations. Due to the young age of the patients, effective communication often requires the involvement of three parties: the doctor, the child, and the parents [42]. This unique communication model brings many challenges, such as parents having difficulty accurately expressing their child's symptoms, children being unable to clearly articulate their discomfort, and cognitive differences between parents and doctors [43]. We can categorize the issues arising in the context of pediatric care into two types. The first type involves unidirectional communication impairment between doctors and infant patients, where infant patients cannot clearly express their symptoms. The second type pertains to the bidirectional communication deviation between doctors and parents, as parents, who are not the patients themselves, find it challenging to convey the patient's symptoms accurately, and there is a certain degree of cognitive bias between parents and doctors [44]. In recent years, with the advancement of artificial intelligence technology, medical large language models (mLLMs) have been introduced as auxiliary tools in pediatric diagnostics, aiming to improve diagnostic efficiency and accuracy. However, despite their ability to process vast amounts of medical data and learn from them, these models still face some fundamental limitations.  
We propose a DIKWP-oriented human-machine interaction model. For the first type of unidirectional communication impairment between doctors and patients, we will attempt to generate and interpret conceptual semantics from the perspective of infants, simulating their cognitive processes in a non-verbal environment [45]. Taking the example of infants recognizing and distinguishing a ball, we analyzed the cognitive process in an infant's brain as it transitions from having no understanding to constructing a semantic space. We describe the scenario, generating corresponding semantics and abstracting concepts and labels to address the unidirectional communication impairment between doctors and patients. For the second type of bidirectional communication biases between doctors and parents, we mapped the DIKWP resources generated by the interacting subjects to the 3-No problem framework and explore the transformation of DIKWP*DIKWP. This provides a more precise display of the reasons behind the communication deviations and suggest improvements. Establishing a bidirectional understanding channel between humans and machines will better accommodate individual users' semantic needs while maintaining efficient and accurate semantic communication across various contexts.  
Section 3 shows the process of constructing DIKWP semantic interactions for unidirectional communication impairments, Section 4 shows the process of DIKWP semantic interactions for bidirectional communication biases, and Section 5 describes the experimental part, where a series of experiments were conducted with the proposed method. Section 6 is the Discussion section, which summarizes the main work of this article as well as its limitations and provides an outlook on future research directions.