Public and nonprofit organizations often hesitate to adopt AI tools because most models are opaque even though standard approaches typically analyze aggregate patterns rather than offering actionable, case-level guidance. This study tests a practitioner-in-the-loop workflow that pairs transparent decision-tree models with large language models (LLMs) to improve predictive accuracy, interpretability, and the generation of practical insights. Using data from an ongoing college-success program, we build interpretable decision trees to surface key predictors. We then provide each tree's structure to an LLM, enabling it to reproduce case-level predictions grounded in the transparent models. Practitioners participate throughout feature engineering, model design, explanation review, and usability assessment, ensuring that field expertise informs the analysis at every stage. Results show that integrating transparent models, LLMs, and practitioner input yields accurate, trustworthy, and actionable case-level evaluations, offering a viable pathway for responsible AI adoption in the public and nonprofit sectors.

Artificial Intelligence (AI) is the theory and development of computer systems that are capable of performing tasks that require human intelligence; in other words, taking elements of what we today consider to be exclusively human traits and transferring them to a machine in a satisfactory manner. These human traits include visual perception, voice recognition, decision making, and translation. In addition, communication, the ability to learn new things, the ability to abstract or associate with new knowledge based on already established knowledge, and a number of other issues are key to the development of artificial intelligence. The machines should also have the same knowledge that we learn in school: the difference between right and wrong. How and what artificial intelligence will be used for in the future is still a question we don't know the answer to.

There is no shortage of advances in AI these days especially as it relates to Deep Learning. From "Everybody dance now" where an AI-based Transfer Motion can make you appear to dance like a star, to an AI-based News Anchor in China that reads the daily news with impressive facial expressions and voice inflection much like a human. For Healthcare there has been much advances in medical imaging analysis from diagnostic imaging, to diabetic retinopathy, etc. to name a few. This is great news, but I believe more can be done, specifically as it relates to the use of AI in physician and hospital settings. The following are 3 practical ways to think about AI in healthcare.

Bon Secours Charity Hospital, a three-hospital health system that is part of Westchester Center Health Network, also known as WMCHealth, was using a risk scoring algorithm in its electronic health record that was not very accurate. As a result, WMCHealth missed some high-risk patients and classified other patients as high-risk who were not. In addition, the automated daily report sent to case managers included only patients who had primary care doctors. The case managers also wasted a lot of effort digging through charts to decide which patients to prioritize and which interventions to select. That reduced the amount of time they had to spend with patients.

We envision an integrated framework for supporting the development and deployment of human-aware, general artificial intelligence (AI) that needs to collaborate in uncertain, changing environments. We examine the technology and system requirements of building assistive care agents for dementia or cognitive impaired patients through the continuum of care. We summarize the new AI capabilities and show examples of how an evolving, adaptive development approach would be able to support the basic functionalities and applications in a sound, practical, and scalable manner. We highlight the challenges and the opportunities involved in realizing the proposed framework, and call for future research and development efforts from the AI community to work in this challenging and important domain.