Efficiency and transparency of artificial intelligence‐driven visual Chatbot: Comment

Abstract

Dear editor, We would like to respond to a comment on the published article entitled “Leveraging the efficiency and transparency of artificial intelligence-driven visual Chatbot through smart prompt learning concept1”. This research highlights the significance of infusing humanistic ideas into education in the context of AI technology, notably in the field of health. It investigates the concept of smart prompt learning within AI visual chatbot platforms such as LLaVA in order to lessen reliance on AI-generated information and increase human decision-making skill. The current case study demonstrates how, using Benner's Theory, multiple levels of healthcare professionals' viewpoints can be evaluated to highlight the importance of human judgment in medical decision-making. The lack of actual evidence or quantitative analysis to substantiate the claims presented is one of this work's flaws. The case study offered is anecdotal in nature and does not provide a complete assessment of the efficacy of smart quick learning.. Furthermore, the research is limited to one field of medicine (dermatology) and does not examine the relevance or limitations of smart fast learning in other medical disciplines. In terms of future directions, controlled studies or experiments to examine the influence of smart rapid learning on medical education and decision-making would be advantageous. Quantitative data can provide more rigorous evidence of this approach's effectiveness. Furthermore, investigating the possible benefits and limitations of merging AI technology with human judgment in other medical specializations would provide a more comprehensive grasp of the subject. Finally, future research should prioritize addressing ethical concerns, such as privacy and data security, in the use of AI tools in medical education and practice. Finally, it is up to each AI system user to determine whether to apply a reasonable and moral code.2 The articles have no fund and ask for waiving for any charge from the journal. Authors declare no conflict of interest. Not applicable. There is no new data generated.

Ähnliche Arbeiten