Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.

BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Notch signaling inhibition protects against LPS mediated osteolysis.

Chronic inflammatory responses have profound effects on the differentiation and activity of both the bone-forming osteoblasts and bone-resorbing osteoclasts. Importantly, inflammatory bone diseases characterized by clinical osteolysis promote bone resorption and decrease bone formation by uncoupling the process in favor of excess resorption. Notch signaling regulates osteoclast development and thus its manipulation has the potential to suppress resorptive potential. Here, we have utilized a genetic model of Notch inhibition in osteoclasts by expression of dnMAML to prevent formation of transcriptional complex essential for downstream Notch signaling. Using this model and LPS as a tool for experimental inflammatory osteolysis, we have demonstrated that dnMAML-expressing osteoclasts exhibited significantly lower maturation and resorption/functional potential ex vivo using TRAP staining and calcium phosphate coated surfaces. Moreover, we observed that while LPS stimulated the formation of wildtype osteoclasts pre-treated with RANKL, dnMAML expression produced resistance to osteoclast maturation after LPS stimulation. Genetically, Notch-inhibited animals showed a significantly lower TRAP and CTX-1 levels in serum after LPS treatment compared to the control groups in addition to a marked reduction in osteoclast surfaces in calvaria sections. This report provides evidence for modulation of Notch signaling activity to protect against inflammatory osteolysis. Taken together, the findings of this study will help guide the development of Notch signaling-based therapeutic approaches to prevent bone loss.