Prior studies have established the active involvement of patients' disparities in the treatment of colorectal cancer (CRC) among underrepresented classes of society. Nevertheless, an examination of these roles using generative AI tools has not been explicitly conducted. Establishing a communication tone between patients and the treatment team for colorectal cancer is an essential component of clinical practice, as it substantially influences the efficacy of colorectal cancer treatment. The objective is to identify the textual tones of the keyphrases commonly used by doctors in answering commonly asked questions by patients during colorectal cancer treatment. We used a scientific article corpus sourced from Medline, Cochrane, the Web of Science, and Pubmed to train a miniature GPT model using KerasNLP. Subsequently, we extracted the predominant keyphrases that are closely linked to these papers using vlT5. These keyphrases will be built into prompts with transformer agents and then fed into the trained miniature model to analyse and determine the tonality of the language using the sentiment analysis approach with BERT. The overall aim of this project is to provide guidance to clinicians regarding their communication style when interacting with underrepresented patients diagnosed with colorectal cancer. The implementation of an effective application model holds the capacity to significantly influence the treatment of colorectal cancer, particularly in terms of patient-centred communications, thereby yielding advantageous patient-centred outcomes for disadvantaged groups.