From chatbots answering patient questions to the rollout of AI tools used to support patient management, the artificial intelligence (AI) era is upon us now and it’s imperative to fully understand the promise – and pitfalls – of this new technology to help guide patient care and counseling. That’s the impetus for this issue’s focus on AI and large language models (LLMs) in health care and how these tools may affect HIV prevention and care. Our authors explore how AI and LLMs can shift workloads, keep rural populations in care, and support the education and training of the next generation of providers.
In “A Day in the Life of an HIV Specialist in the Agentic AI Era,” John J. Hanna, MD, illustrates how HIV clinicians can use AI tools within their practices to help streamline care while giving providers time to give their undivided attention to patients during a visit. He describes processes that are developed thoughtfully with an eye to ensuring human oversight of outputs, and underscores the need for HIV clinicians to engage in this rapidly evolving technology to ensure equitable, human-centered care.
In applying these principles of equitable HIV care, Shameka L. Cody, PhD, AGNP-C, Katherine M. Dudding, PhD, RN, and Brittany C. Sanders, DNP, CRNP, turn their attention to rural patient care and the intersection of AI. In their article “Integrating AI Tools in HIV Prevention and Treatment for Rural and Underserved Communities,” they consider the opportunities that AI offers when trying to address gaps in HIV prevention and care. They highlight how AI can help support rural patients in HIV when it is implemented with intentionality. However, there are still barriers to digital access that rural populations face that must be considered when using this new technology.
AI and LLMs are not only affecting current clinical practice, but they are reaching into the development of the HIV workforce. Justin Hooks, DNP, FNP-BC, addresses this in his article “Teaching in the Age of Generative AI: Opportunity, Risk, and a Path Forward for Health Professions Education.” He posits that educators must embrace AI and LLM tools and learn how to weave them into the curriculum in order to ensure students use these tools with adequate guidance. He gives concrete examples of how to support learning using AI and how to ensure students understand that AI is a tool, not a replacement for critical thinking.
Finally, another area of health care has been implementing AI in their processes and it has the potential to affect patients and clinics alike: insurance and reimbursement. Darius Tahir and Lauren Sausser write about the regulation of AI use by health insurance companies and what’s at stake. In their article, “Red and Blue States Alike Want To Limit AI in Insurance. Trump Wants To Limit the States,” they outline the push to incorporate policy guardrails for AI’s use in insurance and reimbursement, especially in the area of prior authorizations and how states and insurance companies are responding to this push.
Also, in this issue’s At the Forefront section, we feature a new case report of a woman who maintained viral suppression throughout pregnancy while continuing long-acting cabotegravir/rilpivirine injections. In “Maintenance of Viral Suppression with Long-Acting Cabotegravir and Rilpivirine During Pregnancy: A Case Report,” authors Blair Thedinger, MD, and Aaron Sriram Devanathan, PharmD, PhD, present a promising case of the potential role of long-acting CAB/RPV in maintaining viral suppression during pregnancy when a patient experiences difficulties with adherence to oral ART.
