Structure of AI Responses With Complex Patient Analysis for Patient Alternatives and Patient Modifiers

ABSTRACT Background Artificial intelligence (AI) is already a powerful tool that is rapidly growing within the dental sector. Reports of structure and characteristics of AI responses to patient scenarios are limited. Purpose To analyse characteristics and structure of AI responses to prompts for treatment planning alternatives and patient alternatives for complex patients. The project is seen as a prelude to exploring the interface between AI information and responsibility for patient decisions, patient privacy and credibility of AI information. Methods Microsoft Copilot from July 2025 was prompted for a hypothetical patient scenario to develop treatment alternatives. In addition to treatment alternatives, patient analysis factors were prompted and three were analysed in the manuscript: patient modifying factors, patient capacity to subscribe to professional recommendations and prognoses. Results Qualitative analyses for AI responses were extensive, in categories, amenable to table format, informational and not recommendational. For each treatment alternative AI generated goal, phases, pros and cons. AI offered ten modifying factors affecting dental treatment, including age, medical conditions, medications, etc. For patient capacity, AI generated seven responses under headings of positive indicators and limitations. For prognoses, AI generated short‐and long‐term prognoses with key indicators. Treatment alternatives remained largely unchanged before and after sequential inclusion of patient modifying factors. Conclusions AI can offer extensive categorised information on patient care to reinforce the dentist of considerations in patient care without making recommendations. Responsibility for patient decisions, patient privacy and soundness of information remains with the dentist.