Comment on Asir et al. Investigation of Vitamin D Levels in Men with Suspected Infertility. Life 2024, 14, 273.

We have reviewed the article "Investigation of Vitamin D Levels in Men with Suspected Infertility" by Firat Asir [...].

Breaking Barriers in Behavioral Change: The Potential of Artificial Intelligence-Driven Motivational Interviewing.

Patient outcomes in ophthalmology are greatly influenced by adherence and patient participation, which can be particularly challenging in diseases like glaucoma, where medication regimens can be complex. A well-studied and evidence-based intervention for behavioral change is motivational interviewing (MI), a collaborative and patient-centered counseling approach that has been shown to improve medication adherence in glaucoma patients. However, there are many barriers to clinicians being able to provide motivational interviewing in-office, including short visit durations within high-volume ophthalmology clinics and inadequate billing structures for counseling. Recently, Large Language Models (LLMs), a type of artificial intelligence, have advanced such that they can follow instructions and carry coherent conversations, offering novel solutions to a wide range of clinical problems. In this paper, we discuss the potential of LLMs to provide chatbot-driven MI to improve adherence in glaucoma patients and provide an example conversation as a proof of concept. We discuss the advantages of AI-driven MI, such as demonstrated effectiveness, scalability, and accessibility. We also explore the risks and limitations, including issues of safety and privacy, as well as the factual inaccuracies and hallucinations to which LLMs are susceptible. Domain-specific training may be needed to ensure the accuracy and completeness of information provided in subspecialty areas such as glaucoma. Despite the current limitations, AI-driven motivational interviewing has the potential to offer significant improvements in adherence and should be further explored to maximally leverage the potential of artificial intelligence for our patients.

AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. OBJECTIVE: We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption. METHODS: This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected. RESULTS: This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential. CONCLUSIONS: Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.

Chatting Beyond ChatGPT: Advancing Equity Through AI-Driven Language Interpretation.

Medical interpretation is an underutilized resource, despite its legal mandate and proven efficacy in improving health outcomes for populations with low English proficiency. This disconnect can often be attributed to the costs and wait-times associated with traditional means of interpretation, making the service inaccessible and burdensome. Technology has improved access to translation through phone and video interpretation; with the acceleration of artificial intelligence (AI) large language models, we have an opportunity to further improve interpreter access through real-time, automated translation. The impetus to utilize this burgeoning tool for improved health equity must be combined with a critical view of the safety, privacy, and clinical decision-making risks involved. Physicians must be active participants and collaborators in both the mobilization of AI tools to improve clinical care and the development of regulations to mitigate harm.

PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor.

Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x-, y-, and z-axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced.

Development of Autologous C5 Vaccine Nanoparticles to Reduce Intravascular Hemolysis in Vivo.

The complement system is emerging as a new target for treating many diseases. For example, Eculizumab, a humanized monoclonal antibody against complement component 5 (C5), has been approved for paroxysmal nocturnal hemoglobinuria (PNH) in which patient erythrocytes are lysed by complement. In this study, we developed vaccines to elicit autologous anti-C5 antibody production in mice for complement inhibition. Immunization of mice with a conservative C5 xenoprotein raised high titers of IgG's against the xenogenous C5, but these antibodies did not reduce C5 activity in the blood. In contrast, an autologous mouse C5 vaccine containing multiple predicted epitopes together with a tolerance-breaking peptide was found to induce anti-C5 autoantibody production in vivo, resulting in decreased hemolytic activity in the blood. We further validated a peptide epitope within this C5 vaccine and created recombinant virus-like particles (VLPs) displaying this epitope fused with the tolerance breaking peptide. Immunizing mice with these novel nanoparticles elicited strong humoral responses against recombinant mouse C5, reduced hemolytic activity, and protected the mice from complement-mediated intravascular hemolysis in a model of PNH. This proof-of-concept study demonstrated that autologous C5-based vaccines could be an effective alternative or supplement for treating complement-mediated diseases such as PNH.