Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction

Abstract

Objective: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients.Methods: The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients’ various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed.Results: The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively.Conclusion: This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care…

“…Multiple groups have already put various conversational AI platforms to use them foradvance patient care including taking down medical histories, assisting in psychotherapy, and improving care in chronic conditions. 7,[13][14][15][16] However, the conventional conversational AI platforms were limited with mostly structured rules of engagement. ChatGPT has new potential roles in both patient care and research including producing primary documents and assisting in research conceptualization/design.…”

“…fforts to apply artificial intelligence (AI) and machine learning in the medical field and specifically in neurosurgery are actively underway. [1][2][3][4][5][6][7] Currently, imaging-based diagnosis, disease diagnosis, and methods of prediction using clinical data and genomic data are fields that receive the most attention. [3][4][5][6][8][9][10][11] However, AI technologies associated with natural language processing are also being investigated.…”

Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning

“…The inclination towards firstorder functions arose more from their simplicity than their precision. 1,4,[11][12][13][14][15] Considering the intricate interplay of factors influencing spinal sagittal alignment, including the whole spine, pelvis, age-related alignment adjustments, and compensatory mechanisms, a simple first-order function seems insufficient. AI-driven computations, equipped with sophisticated analyses involving advanced mathematical techniques encompassing multivariable and higher-order functions, are suggested to surpass classical regression analyses in terms of accuracy.…”