**1. Introduction**

Although Artificial Intelligence (AI) in Healthcare has recently become trendy, the concept is not new. Alan Turning developed the concept of machines that could think around the 1950's [1]. Soon thereafter, John McCarthy proposed the term "Artificial Intelligence" to describe the process of computers that could perform the cognitive functions of humans. Since these early propositions healthcare has seen a monumental increase in data available for interpretation. Consequently, the power and usefulness of computers in data analysis has become paramount to the success of a healthcare organization as it is unrealistic for individuals and even highly organized teams to extrapolate important information. Subsequently, various medical societies and disciplines have invested heavily in AI to meet the growing demands of modern medicine. Alarmingly, Family Medicine appears to lag behind other specialties in advancing its footprint in the AI healthcare space. Specifically, the American Board of Family Medicine performed an extensive literature review in the year 2020 and found

no publications for this specialty during that time despite knowledge that Family Medicine scholars were actively pursuing research related to Primary Care and AI [2].

The importance of the discipline of Family Medicine being actively involved in AI research cannot be understated as historically this profession has lagged behind when the adoption of new technology takes place. Subsequently, the discipline and more importantly, the patients have needlessly suffered for it. For example, when the Health Information Technology for Economic and Clinical Health (HITECH) Act was passed, it was widely assumed the introduction of electronic medical records (EMR) would enhance the patient, physician, and organizational experience through the optimization of efficient, equitable, and effective healthcare delivery [3]. Certainly, EMR's have had numerous positive impacts on an individual patient and systemsbased perspective [4]. No one would rightly argue for a return to paper charts and hand-written notes. Nevertheless, we cannot ignore the role the implementation of EMR has had on increased physician burnout and decreased face-to-face time with patients. Moreover, because of the lack of Family Medicine involvement in the roleout of EMR's many in our field strongly feel as though its usability, interoperability, and applicability have fallen short of the initial intended goals of EMR. This is likely due to lack of engagement from family physicians in the design, advocacy, and implementation of EMR. Accordingly, there is a rising concern that healthcare technology has grown to suit hospital administrators more than patients and physicians [4].

With the advancement of AI, the specialty of Family Medicine must be an active participant to further influence this transformation. The relationship-oriented nature of Family Medicine will allow for technology to focus on providing value to patients and communities as opposed to administrators and technology companies. Healthcare costs continue to escalate and without FM providers who are focused on providing value AI will likely only exacerbate the sentiment that only those who can afford such advances in healthcare will benefit from it. The ethos of Family Medicine is that the development of the therapeutic relationship optimizes treatment outcomes and positively effects health on a population level. Without this belief AI will only further reduce the patient-physician interaction through increased screen-time. Family Medicine practitioners pride themselves on seeing a diverse patient panel. Consequently, if Family Physicians voices are not heard AI may amplify existing biases. Specifically, algorithms used by recognition programs have demonstrated challenges in recognizing persons of color secondary to limited participation [5].

Computers process information faster, more efficiently, and more systematically than humans. They make judgments more regularly and act in response to variations faster. Currently, computers perform automated repetitive tasks once assigned to humans. Clinical decision support systems alert providers when immunizations are due, automatic American Society for Cardiovascular Disease (ASCVD) risk calculators provide myocardial infarction risk assessment, and advertisements for potential drug–drug or allergic reactions pop up before a medication may be administered or prescribed. Moreover, AI can already complete challenging multifactorial jobs to create an accurate differential diagnosis and evidence-based assessment and plan. Worryingly, machines autonomously managing patients may give administrators pause as to the value of human physicians.

Family Practitioner engagement in AI is a must. Primary care offers the grandest healthcare distribution platform and provides an influential stage for data use [6]. Family Practitioners are operations specialists who may practice approaches for the adoption of scientifically validated AI tools. Family Medicine practitioners focus on patient-oriented outcomes and will publish results that affect the patients [7].
