Artificial Intelligence Expert Research Articles

Stakeholder perspectives on ethical and trustworthy voice AI in health care.

Voice as a health biomarker using artificial intelligence (AI) is gaining momentum in research. The noninvasiveness of voice data collection through accessible technology (such as smartphones, telehealth, and ambient recordings) or within clinical contexts means voice AI may help address health disparities and promote the inclusion of marginalized communities. However, the development of AI-ready voice datasets free from bias and discrimination is a complex task. The objective of this study is to better understand the perspectives of engaged and interested stakeholders regarding ethical and trustworthy voice AI, to inform both further ethical inquiry and technology innovation. A questionnaire was administered to voice AI experts, clinicians, scholars, patients, trainees, and policy-makers who participated at the 2023 Voice AI Symposium organized by the Bridge2AI-Voice AI Consortium. The survey used a mix of Likert scale, ranking and open-ended questions. A total of 27 stakeholders participated in the study. The main results of the study are the identification of priorities in terms of ethical issues, an initial definition of ethically sourced data for voice AI, insights into the use of synthetic voice data, and proposals for acting on the trustworthiness of voice AI. The study shows a diversity of perspectives and adds nuance to the planning and development of ethical and trustworthy voice AI. This study represents the first stakeholder survey related to voice as a biomarker of health published to date. This study sheds light on the critical importance of ethics and trustworthiness in the development of voice AI technologies for health applications.

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools.

Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competences from the optional ones. The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional. This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.

Enhancing the Competitiveness of AI Technology-Based Startups in the Digital Era

Artificial Intelligence (AI) startups possess four key attributes; being small enterprises, adopting AI technology, undergoing digital transformation, and using big data systems to enhance their competitiveness. This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era. To achieve this, the hierarchy concept framework was built with four evaluation areas based on the mechanism-based view theory, and the 16 evaluation factors that can influence were identified through existing literature, combining factors related to the digital transformation, technological application, and business competitiveness of the startups. These factors were analyzed using the Analytic Hierarchy Process (AHP) by the survey, targeting experts in South Korea. The analysis results indicate that the subject area was the most crucial for the business competitiveness of AI startups. It was also revealed that the subject’s strategic mind is the most significant factor to AI startups’ success. In the case of two control groups, categorized as ‘AI experts’ and ‘startup experts’, AI experts chose the subject as the most important area, whereas startup experts selected the environment, and significant differences were observed in all other factors. The results of this study will provide implications for strengthening the business competitiveness of AI startups and factors important for the growth of AI startups in this era.

Converging perspectives: Assessing AI readiness and utilization in Philippine higher education

In rapidly evolving landscape of technology, the integration of artificial intelligence (AI) has become prevalent, reshaping various facets of students’ lives. This study delved into the uncharted territory of AI awareness, utilization, and perceptions among college students. The study used a convergent parallel mixed-methods design, integrating quantitative survey data with qualitative responses in order to get insights on the impact of AI in education and society. The survey found that college students familiarity on AI depends on age, academic year, and field of study. This emphasizes the need for targeted AI education to overcome knowledge inequalities, particularly among younger cohorts and in fields with little AI expertise. AI usage is usually modest for academic and personal purposes, while insights includes its uses in academic research, job administration, and language translation. The varied application of AI requires institutions to adapt their procedures, and societal impacts which are largely seen positively. This optimism is tempered by concerns about job loss, data privacy breach, technological overuse, and human decision-making. Thus, comprehensive AI education programs are needed to address and traverse these varied opinions. The results emphasized the necessity for institutions and policymakers to be proactive when artificial intelligence is transforming many companies and social systems. Students must learn AI literacy, combining knowledge and practical application, to navigate the AI-driven environment's complexity and opportunities. The endeavor requires teaching technical skills and a deep grasp of AI's social and ethical implications. Institutions can prepare students for a future where artificial intelligence is becoming more important by understanding these effects and tailoring teaching.

Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework

Online misinformation is posing a serious threat for the modern society. Assessing the veracity of online information is a complex problem which nowadays is addressed by heavily relying on trained fact-checking experts. This solution is not scalable, and due to the importance of the problem the issue gained the attention of the scientific community, which proposed many based on Artificial Intelligence and Machine Learning methods. Despite the efforts made, the effectiveness of such approaches is not yet enough to allow them to be used without supervision. In this position paper, we propose a hybrid human-in-the-loop framework for fact-checking: we address the misinformation issue by relying on a combination of automatic Artificial Intelligence methods, crowdsourcing ones, and experts. We study the single components of the framework as well as their interactions, and we propose an interleaving of the different components which we believe will serve as a useful starting point for the future research towards effective and scalable fact-checking.

Disinformation and Artificial Intelligence: Looking at Ways to Combat Disinformation through Artificial Intelligence Experts’ Eyes

İletişim teknolojilerindeki gelişim ve kullanıcı kaynaklı içeriğin yükselişi, her türlü içeriği herhangi bir kontrol mekanizmasına takılmadan kolaylıkla dolaşıma sokulabilir kılmıştır. Bu durum, günümüzde dijital platform kullanıcılarının sınırsız sayıda içeriğe hızlı erişimini sağlamakla birlikte; bireylerin maruz kaldıkları yoğun dezenformasyonu da beraberinde getirmiştir. Çevrimiçi dezenformasyonla mücadele süreci, yapay zekâ tekniklerinin kullanımıyla yakından ilişkilenmekte; söz konusu teknoloji hem dezenformasyonun üretilip yaygınlaştırılmasında hem de sorunlu içeriğin tespiti ve denetiminde önemli bir rol üstlenmektedir. Dezenformasyon ve yapay zekâ ilişkisinin bu iki yönü, yapay zekâ teknolojilerinin sorunlu içeriğin üretimi ve dağıtımı sürecindeki belirleyiciliğinin ve çevrimiçi dezenformasyonun tespit edilip azaltılabilmesi için yapay zekâ sistemlerinden en efektif biçimde nasıl yararlanılabileceğinin anlaşılmasını da gerekli kılmaktadır. Bu odak noktasından hareketle gerçekleştirilen çalışma kapsamında, yapay zekâ sistemlerinin dezenformasyonla mücadele sürecindeki potansiyelinin yapay zekâ uzmanlarının gözünden değerlendirilmesi hedeflenmektedir. Bu hedef doğrultusunda, Yapay Zekâ Politikaları Derneği (AIPA) üyesi ve paydaşı olan yapay zekâ uzmanlarıyla yarı yapılandırılmış görüşme tekniğinin kullanıldığı betimsel nitelikli bir alan araştırması gerçekleştirilmiştir. Çalışma sonucunda, günümüz yapay zekâ sistemlerinin dezenformasyonun artırılmasında olduğu kadar azaltılması için de nasıl aktif kullanılabileceği; bunun için dezenformasyon tespit ve filtreleme mekanizmalarının, doğrulama platformlarının yaygınlaştırılmasının gerekliliği, bu amaçla geliştirilecek politikalar kamu-dijital platform iş birliğiyle oluşturulurken kullanıcıya karşı sorumluluğun da öncelenmesine ihtiyaç duyulduğu tespit edilmiştir.

Identifying the opportunities and challenges of artificial intelligence in higher education: a qualitative study

PurposeThis research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education.Design/methodology/approachThis qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted.FindingsThe authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education.Research limitations/implicationsConcerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc.Originality/valueThe innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education.

Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub.

The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical tasks. This environment should be open for peer testing, feedback, and continuous model refinement, allowing dynamic model updates that are especially important in the medical field, where diseases and scanning techniques evolve rapidly. In this context, the Discovery Viewer (DV) platform was developed in-house at the Biomedical Engineering and Imaging Institute at Mount Sinai (BMEII) to facilitate the creation and distribution of cutting-edge medical AI models that remain accessible after their development. The all-in-one platform offers a unique environment for non-AI experts to learn, develop, and share their own deep learning (DL) concepts. This paper presents various use cases of the platform, with its primary goal being to demonstrate how DV holds the potential to empower individuals without expertise in AI to create high-performing DL models. We tasked three non-AI experts to develop different musculoskeletal AI projects that encompassed segmentation, regression, and classification tasks. In each project, 80% of the samples were provided with a subset of these samples annotated to aid the volunteers in understanding the expected annotation task. Subsequently, they were responsible for annotating the remaining samples and training their models through the platform's "Training Module". The resulting models were then tested on the separate 20% hold-off dataset to assess their performance. The classification model achieved an accuracy of 0.94, a sensitivity of 0.92, and a specificity of 1. The regression model yielded a mean absolute error of 14.27 pixels. And the segmentation model attained a Dice Score of 0.93, with a sensitivity of 0.9 and a specificity of 0.99. This initiative seeks to broaden the community of medical AI model developers and democratize the access of this technology to all stakeholders. The ultimate goal is to facilitate the transition of medical AI models from research to clinical settings.

Recent Advances and Future Challenges in Pancreatic Cancer Care: Early Detection, Liquid Biopsies, Precision Medicine and Artificial Intelligence

The incidence of pancreatic ductal adenocarcinoma (PDAC) is rising. While surgical techniques and peri-operative care have improved, the overall survival for PDAC remains poor. Thus, novel and bold research initiatives are needed along the spectrum of clinical care, a few of which will be discussed in this article. Early detection is crucial, with specific high-risk groups possibly benefiting from targeted screening programs. Liquid biopsies (such as circulating exosomes, tumor DNA, or tumor cells) offer promise as multifunctional biomarkers for early detection, treatment guidance, and recurrence monitoring. Precision medicine is being explored via targeted therapies for actionable mutations, such as PARP inhibitors for BRCA mutations, and immunotherapy strategies. Artificial intelligence (AI) is emerging as a powerful tool in medical imaging, biomarker discovery, genetics research, and treatment planning, and it can aid in diagnosis, treatment selection, and patient monitoring. However, its associated challenges include ethics, data security, algorithm reliability, and validation. Collaborative efforts between medical professionals, researchers, and AI experts are vital for unlocking AI’s potential to enhance pancreatic cancer care. In conclusion, despite the challenges, advancements in liquid biopsies, precision medicine, and AI offer hope for enhancing the diagnosis, treatment, and management of pancreatic cancer.

Evaluating international AI skills policy: A systematic review of AI skills policy in seven countries

AbstractAs artificial intelligence (AI) is having an increasingly disruptive impact across industries, companies continue to report having difficulty when recruiting for AI roles, while new graduates find it difficult to find employment, indicating a skills gap or skills misalignment. International approaches to AI skills programmes can offer a guide to future policy development of a skilled workforce, best placed to harness the economic opportunities that AI may support. The authors performed a systematic literature review on AI skills in government policies and documents from seven countries: Australia, Canada, China, Singapore, Sweden, the United Kingom and the United States. We found a divide between countries which emphasised a broader, nationwide approach to upskill and educate all citizens at different levels, namely the United States and Singapore and those countries which emphasised a narrower focus on educating a smaller group of experts with advanced AI knowledge and skills, namely China, Sweden and Canada. We found that the former, broader approaches tended to correlate with higher AI readiness and index scores than the narrower, expert‐driven approach. Our findings indicate that, to match world‐leading AI readiness, future AI skills policy should follow these broad, nationwide approaches to upskill and educate all citizens at different levels of AI expertise.

Addressing Bio-Cybersecurity Workforce Employee Shortages in Biotechnology and Health Science Sectors in the U.S.

Abstract The ever-increasing demand for employees with cybersecurity and artificial intelligence expertise in healthcare technology roles has resulted in an unprecedented skills shortage. Historically Black Colleges and Universities (HBCUs) and Historically Women’s Colleges (HWCs) can significantly develop under-represented and diverse talent in this field. As bio-cybersecurity concerns widen, there is an urgent need for individuals with skillsets related to the security of biological payloads and systems. In addition, the inclusion of traditionally under-represented groups into the cybersecurity workforce is necessary to ensure the diversity of skills and security knowledge needed to combat cyberattacks. This paper will discuss how HBCUs and HWCs can contribute to diversifying the cybersecurity workforce, their role in developing artificial intelligence (AI) and bio-cybersecurity talent, and how they can provide an educational platform for new and under-represented talent in the field. It will also address the need for experienced artificial intelligence experts, bio-cybersecurity professionals, educators, and policymakers to facilitate the integration of cybersecurity experts into the traditional biological workforce. Ultimately, this paper will demonstrate how the inclusion of diverse and traditionally under-represented individuals in bio-cybersecurity and artificial intelligence (AI) will benefit the security infrastructure of biological systems.

Emergency department care transitions for persons living with cognitive impairment and care partners: development of a novel artificial intelligence and care coach intervention

AbstractBackgroundEmergency department (ED) presentation by persons living with cognitive impairment (PLWCI) often represents a sentinel event in the care continuum, yet little data exists to date regarding effective interventions to improve ED‐to‐community care transitions for PLWCI and their care partners. Therefore, we sought to develop and refine the innovative pairing of artificial intelligence (AI)‐enabled digital advisors and an occupational therapist‐led care coach intervention to improve ED‐to‐community care transitions for PLWCI and their care partners.MethodsWe used a mixed methods, multi‐phased approach to develop the intervention, with PLWCI and care partners sampled from the LiveWell Dementia Specialists network. In the first phase, we developed and iteratively revised a codebook and conducted recorded, semi‐structured focus groups using a standardized guide. Two researchers coded the professionally transcribed data using a combined deductive and inductive approach and analyzed transcripts to identify dominant themes and representative quotations. In the second phase, we performed a 2‐day design thinking workshop using a user‐centered design approach, involving PLWCI, care partners, health care professionals, community‐based partner organizations, technologists, and AI experts. Integration of findings from the two phases provided takeaways for refinement and optimization of the intervention for eventual pilot testing.ResultsIn the first phase, we performed two focus groups, involving a total of 16 participants, and generated empathy maps regarding the ED experience, the subsequent ED‐to‐community care transition, and the role of technology. PLWCI and care partners expressed: 1) concerns that their unique needs related to cognition were not addressed in the ED setting, 2) frustration with the ED discharge process and their perceived ability to follow‐up in the outpatient setting and obtain desired resources, and 3) openness to the incorporation of AI to facilitate successful ED‐to‐community care transitions. In the second phase, we identified perceived barriers and facilitators for intervention adoption during open forum discussions, depicted the current ‘As Is’ and the desired ‘To Be’ states regarding ED‐to‐community care transitions, and provided a demonstration of the AI‐enabled digital advisors and care coach intervention.ConclusionPLWCI and care partners were integrally engaged in the co‐creation of a two‐component novel intervention to improve ED‐to‐community care transitions.

Identifying the components of foundational Artificial Intelligence (AI) literacy - Early results from a Delphi study

This article provides some initial results from the first phase of a Delphi study to identify the critical components of an Artificial Intelligence (AI) literacy curriculum. This article explores the study results that address a four-level capability model, but only the lowest level of this model. The Delphi panel comprised 17 experts in AI, and the first round of the study used a survey to gather the experts' responses in three areas that were based on previous literature - knowledge (and concepts) of AI, skills related to AI, and understanding AI in context. A structured thematic analysis revealed several themes under these categories. For foundational knowledge and concepts, it was determined that three areas were needed, namely 1) what is AI? 2) applications of AI, and 3) AI technologies. Skills were divided into cognitive and technical skills, with cognitive skills further divided into 21st-century and applied skills. Understandings comprised social issues, risks, and debates. The repeated ideas that formed these themes gave rich insights into how an AI literacy curriculum might be structured and provided a firm foundation for subsequent rounds of the study, which will involve further iteration and consolidation of these ideas.

Identifying Plant Pests and Diseases with Artificial Intelligence: A Short Comment

Advances in artificial intelligence (AI) have the potential to transform the way we identify and monitor plant pests and diseases. However, there are several challenges that need to be addressed, including the need for large, high-quality datasets, the requirement for expertise in AI and data science, and the development of more sophisticated algorithms and methods. As these challenges are addressed, there is likely to be an increasing focus on the application of AI in the agricultural sector, including plant disease diagnosis.

Explainable artificial intelligence in emergency medicine: an overview.

Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a "black box," leading to trust issues. To address this, "explainable AI," which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of "explainable AI" for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.

Aligning Translation Curricula with Technological Advancements; Insights from Artificial Intelligence Researchers and Language Educators

This study expounded on methods, approaches, and strategies for aligning translation curricula with technological enhancements in the effort to train translators who are good to function in the AI-driven translation industry. The study sample consisted of 83 university lecturers in translation studies and 151 artificial intelligence experts from ten Jordanian universities. The study was carried out during the academic year 2022-2023. Moreover, the study design is a survey approach, and the strategy of the research is quantitative. Data was collected using a questionnaire and analysis was conducted using relevant descriptive statistics tools, including the percentile values of the Likert scale, the mean, and the standard deviation. In addition, the findings generally indicate that task-based approach, team-based strategy, blended learning, and reflective system are the main pedagogical strategies and teaching techniques that work optimally for incorporating AI-based translation technologies into translation curricula. Similarly, the findings generated from the data analysis further suggest that the main strategies to ensure alignment of translation curriculum with technological advancements are careful designing of specific lessons for technology in translation, formation of partnerships between industry players (translation industry leaders and AI experts), and translation curriculum designers, continuous review of the curriculum, and inclusion of creativity and critical thinking for the students. Finally, it is concluded that translation curriculum designers must always review the translation curriculum, partner with translation industry leaders and AI experts, and integrate critical thinking and creativity into the translation curriculum system. This is to ensure that the trained translators can function effectively in the AI-driven translation industry.

Attitudes and perceptions regarding algorithmic judicial judgement: barriers to innovation in the judicial system?

This study aims to be a starting point in the process of studying attitudes and perceptions around the use of algorithmic tools in the judicial system and explores possible barriers to innovation. The results reveal significant differences in acceptance between the general population, experts in data analytics and artificial intelligence (AI) and legal professionals, with notable variations in the acceptance of algorithmic tools for judicial analysis. In addition, participants with a legal background showed a negative correlation with acceptance, indicating a more cautious stance towards the integration of such tools in the criminal justice domain. This suggests a cautious and reserved attitude among legal professionals towards the integration of algorithmic tools in the justice system, potentially rooted in concerns regarding objectivity, fairness and the preservation of legal principles in judicial processes. Furthermore, the study reveals that the acceptance of algorithmic tools is influenced by the complexity of the tasks involved in the automation of the criminal justice system. This underlines the importance of considering the level of automation and the degree of human intervention in the use of these tools. In short, this study highlights the importance of having the choice of society and more specifically of legal operators to encourage the adoption and effective implementation of algorithmic tools in the judicial sphere.

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring.

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

Advancements in early detection of diabetes and diabetic retinopathy screening using artificial intelligence

This paper explores the synergy between early diabetes detection and artificial intelligence (AI)-based diabetic retinopathy screening. It emphasizes the crucial role of integration for enhancing patient care. Diabetes and its complications, such as diabetic retinopathy, can be better managed with early intervention. AI techniques are revolutionizing medical diagnostics, offering potential for accurate prediction of diabetes risk and automated analysis of retinal images for retinopathy detection. By combining these aspects, we uncover a comprehensive strategy that not only identifies diabetes in its early stages but also ensures timely management of its ocular complications. This integrated approach shows promise for personalized care, improved outcomes, and efficient resource utilization. This paper underscores the need for collaboration between medical and AI experts to maximize the potential of this dual-focused approach for transforming diabetes management.

The Need forArtificial Intelligence Curriculum inMilitary Medical Education.

The success of deep-learning algorithms in analyzing complex structured and unstructured multidimensional data has caused an exponential increase in the amount of research devoted to the applications of artificial intelligence (AI) in medicine in the past decade. Public release of large language models like ChatGPT the past year has generated an unprecedented storm of excitement and rumors of machine intelligence finally reaching or even surpassing human capability in detecting meaningful signals in complex multivariate data. Such enthusiasm, however, is met with an equal degree of both skepticism and fear over the social, legal, and moral implications of such powerful technology with relatively little safeguards or regulations on its development. The question remains in medicine of how to harness the power of AI to improve patient outcomes by increasing the diagnostic accuracy and treatment precision provided by medical professionals. Military medicine, given its unique mission and resource constraints,can benefit immensely from such technology. However, reaping such benefits hinges on the ability of the rising generations of military medical professionals to understand AI algorithms and their applications. Additionally, they should strongly consider working with them as an adjunct decision-maker and view them as a colleague to access and harness relevant information as opposed to something to be feared. Ideas expressed in this commentary were formulated by a military medical student during a two-month research elective working on a multidisciplinary team of computer scientists and clinicians at the National Library of Medicine advancing the state of the art of AI in medicine. A motivation to incorporate AI in the Military Health System is provided, including examples of applications in military medicine. Rationale is then given for inclusion of AI in education starting in medical school as well as a prudent implementation of these algorithms in a clinical workflow during graduate medical education. Finally, barriers to implementation are addressed along with potential solutions. The end state is not that rising military physicians are technical experts in AI; but rather that they understand how they can leverage its rapidly evolving capabilities to prepare for a future where AI will have a significant role in clinical care. The overall goal is to develop trained clinicians that can leverage these technologies to improve the Military Health System.