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BACKGROUND: Few studies have examined the performance of artificial intelligence (AI) content detection in scientific writing. This study evaluates the performance of publicly available AI content detectors when applied to both human-written and AI-generated scientific articles. METHODS: Articles published in Annals of Surgical Oncology (ASO) during the year 2022, as well as AI-generated articles using OpenAI's ChatGPT, were analyzed by three AI content detectors to assess the probability of AI-generated content. Full manuscripts and their individual sections were evaluated. Group comparisons and trend analyses were conducted by using ANOVA and linear regression. Classification performance was determined using area under the curve (AUC). RESULTS: A total of 449 original articles met inclusion criteria and were evaluated to determine the likelihood of being generated by AI. Each detector also evaluated 47 AI-generated articles by using titles from ASO articles. Human-written articles had an average probability of being AI-generated of 9.4% with significant differences between the detectors. Only two (0.4%) human-written manuscripts were detected as having a 0% probability of being AI-generated by all three detectors. Completely AI-generated articles were evaluated to have a higher average probability of being AI-generated (43.5%) with a range from 12.0 to 99.9%. CONCLUSIONS: This study demonstrates differences in the performance of various AI content detectors with the potential to label human-written articles as AI-generated. Any effort toward implementing AI detectors must include a strategy for continuous evaluation and validation as AI models and detectors rapidly evolve.
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As cancer therapies increase in effectiveness and patients' life expectancies improve, balancing oncologic efficacy while reducing acute and long-term cardiovascular toxicities has become of paramount importance. To address this pressing need, the Cardiology Oncology Innovation Network (COIN) was formed to bring together domain experts with the overarching goal of collaboratively investigating, applying, and educating widely on various forms of innovation to improve the quality of life and cardiovascular healthcare of patients undergoing and surviving cancer therapies. The COIN mission pillars of innovation, collaboration, and education have been implemented with cross-collaboration among academic institutions, private and public establishments, and industry and technology companies. In this report, we summarize proceedings from the first two annual COIN summits (inaugural in 2020 and subsequent in 2021) including educational sessions on technological innovations for establishing best practices and aligning resources. Herein, we highlight emerging areas for innovation and defining unmet needs to further improve the outcome for cancer patients and survivors of all ages. Additionally, we provide actionable suggestions for advancing innovation, collaboration, and education in cardio-oncology in the digital era.
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OBJECTIVES: Undergraduate college pathway (or pipeline) programs support students' interests as they explore advanced degree and career pathways. Many programs aim to diversify the medical workforce by reducing barriers that may have otherwise prevented desired academic and career goals; however, variability in structure, expectations, benefits, and outcome data exist. This systematic review was conducted to identify and evaluate undergraduate college pathway programs designed to increase the diversity of medical school matriculants. METHODS: We searched Ovid Medline, PsycInfo, Scopus, and the Education Resources Information Center for peer-reviewed, original research publications (1996-2019) describing US pathway/pipeline programs designed for undergraduate-level college students from underrepresented groups to apply and enter medical school. Data extraction included application processes, participant demographics, curricular components, social support systems, mentorship, funding, and program/participant outcomes. We reviewed the journal impact factor to inform us about where articles are being published. RESULTS: Our full-text review included 137 articles; 25 articles met the inclusion criteria. All of the papers were descriptive, requiring an application, minimum grade point average, letters of recommendation, and personal statements. All of the programs aimed to diversify medicine, yet some could not request identification of race/ethnicity because of changes in affirmative action or legal restrictions when reporting demographics. Women represented the majority of enrollees. The program length varied; all reported having one or a combination of academic enrichment, research, field observation/experience, and mentorship. All of the programs included career development and various supplemental social supports. Only two programs provided comparison data; four reported no outcomes. CONCLUSIONS: Pathway programs support the acquisition and enhancement of professional skills. Lacking longitudinal or comparison data leads to questions of the long-term impact on diversifying the medical workforce. This article highlights a need for rigorous data collection methods and transparent reporting of participant outcomes to inform programmatic efficacy.
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Medicine , Students , Humans , Female , Health Personnel/education , Ethnicity , MentorsABSTRACT
BACKGROUND: Millions of cancer survivors are at risk of cardiovascular diseases, a leading cause of morbidity and mortality. Tools to potentially facilitate implementation of cardiology guidelines, consensus recommendations, and scientific statements to prevent atherosclerotic cardiovascular disease (ASCVD) and other cardiovascular diseases are limited. Thus, inadequate utilization of cardiovascular medications and imaging is widespread, including significantly lower rates of statin use among cancer survivors for whom statin therapy is indicated. METHODS: In this methodological study, we leveraged published guidelines documents to create a rules-based tool to include guidelines, expert consensus, and medical society scientific statements relevant to point of care cardiovascular disease prevention in the cardiovascular care of cancer survivors. Any overlap, redundancy, or ambiguous recommendations were identified and eliminated across all converted sources of knowledge. The integrity of the tool was assessed with use case examples and review of subsequent care suggestions. RESULTS: An initial selection of 10 guidelines, expert consensus, and medical society scientific statements was made for this study. Then 7 were kept owing to overlap and revisions in society recommendations over recent years. Extensive formulae were employed to translate the recommendations of 7 selected guidelines into rules and proposed action measures. Patient suitability and care suggestions were assessed for several use case examples. CONCLUSION: A simple rules-based application was designed to provide a potential format to deliver critical cardiovascular disease best-practice prevention recommendations at the point of care for cancer survivors. A version of this tool may potentially facilitate implementing these guidelines across clinics, payers, and health systems for preventing cardiovascular diseases in cancer survivors. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05377320.
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BACKGROUND: The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. OBJECTIVES: To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. DESIGN: This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. This trial will determine whether a clinical decision aid tool improves cancer survivors' medication use and imaging surveillance recommendations aligned with current medical guidelines. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05377320.
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Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.
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PURPOSE OF REVIEW: Cardiovascular disease (CVD) and cancer are the first and second most common causes of death within the USA. It is well established that a diagnosis of cancer increases risk and predisposes the patient to CVD, and vice versa. Despite these associations, cancer is not yet incorporated into current CVD risk calculators, necessitating additional CV risk markers for improved stratification in this at-risk population. In this review, we consider the utility of breast arterial calcification (BAC), coronary artery calcification (CAC), clonal hematopoiesis of indeterminate potential (CHIP), and cancer and cancer treatment in CVD risk assessment. RECENT FINDINGS: There is evidence supporting the use of BAC, CAC, CHIP, and cancer and cancer treatment for improved CV risk stratification in patients with cancer and those who are being screened for cancer. BAC has been shown to predict CAC, coronary atherosclerotic plaque on coronary CTA, coronary artery stenosis on coronary angiography, and CVD events and accordingly enhances CVD risk stratification beyond the atherosclerotic CVD (ASCVD) risk pooled cohort equation. Additionally, CAC visualized on CT utilized for lung cancer screening, radiation planning, and cancer staging is predictive of coronary artery disease (CAD). Furthermore, CHIP can also be utilized in risk stratification, as the presence of CHIP carries a 40% increase in CV risk independent of traditional CV risk factors. Finally, cancer and many oncologic therapies confer a lifelong increased risk of CVD. We propose an emerging set of tools to be incorporated into the routine continuum of CVD risk assessment in individuals who have been treated for cancer or who are being screened for cancer development. In this review, we discuss BAC, CAC, CHIP, and cancer and cancer treatment as emerging risk markers in cardiovascular health assessment. Their effectiveness in predicting and influencing the burden of CVD will be discussed, along with suggestions on their incorporation into preventive cardio-oncology practice. Future research will focus on short- and long-term CVD outcomes in these populations.
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Cardiovascular Diseases , Coronary Artery Disease , Lung Neoplasms , Vascular Calcification , Cardiovascular Diseases/epidemiology , Cardiovascular Diseases/etiology , Cardiovascular Diseases/prevention & control , Coronary Angiography , Coronary Artery Disease/therapy , Early Detection of Cancer , Humans , Lung Neoplasms/complications , Risk Assessment , Risk Factors , Vascular Calcification/diagnostic imaging , Vascular Calcification/epidemiologyABSTRACT
Study objective: Cancer and heart disease are leading causes of mortality, and cardio-oncology is emerging as a new field addressing the cardiovascular toxicities related to cancer and cancer therapy. Interdisciplinary research platforms that incorporate digital health to optimize cardiovascular health and wellness in cancer survivors are therefore needed as we advance in the digital era. Our goal was to develop the Connected Health Innovation Research Program (C.H.I.R.P.) to serve as a foundation for future integration and assessments of adoption and clinical efficacy of digital health tools for cardiovascular health and wellness in the general population and in oncology patients. Design/setting/participants: Partner companies were identified through the American Medical Association innovation platform, as well as LinkedIn and direct contact by our team. Company leaders met with our team to discuss features of their technology or software. Non-disclosure agreements were signed and data were discussed and obtained for descriptive or statistical analysis. Results: A suite of companies with technologies focused on wellness, biometrics tracking, audio companions, oxygen saturation, weight trends, sleep patterns, heart rate variability, electrocardiogram patterns, blood pressure patterns, real-time metabolism tracking, instructional video modules, or integration of these technologies into electronic health records was collated. We formed an interdisciplinary research team and established an academia-industry collaborative foundation for connecting patients with wellness digital health technologies. Conclusions: A suite of software and device technologies accessible to the cardiology and oncology population has been established and will facilitate retrospective, prospective, and case research studies assessing adoption and clinical efficacy of digital health tools in cardiology/oncology.
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PURPOSE OF REVIEW: Cardiovascular toxicity is a leading cause of mortality among cancer survivors and has become increasingly prevalent due to improved cancer survival rates. In this review, we synthesize evidence illustrating how common cancer therapeutic agents, such as anthracyclines, human epidermal growth factors receptors (HER2) monoclonal antibodies, and tyrosine kinase inhibitors (TKIs), have been evaluated in cardiomyocytes (CMs) derived from human-induced pluripotent stem cells (hiPSCs) to understand the underlying mechanisms of cardiovascular toxicity. We place this in the context of precision cardio-oncology, an emerging concept for personalizing the prevention and management of cardiovascular toxicities from cancer therapies, accounting for each individual patient's unique factors. We outline steps that will need to be addressed by multidisciplinary teams of cardiologists and oncologists in partnership with regulators to implement future applications of hiPSCs in precision cardio-oncology. RECENT FINDINGS: Current prevention of cardiovascular toxicity involves routine screenings and management of modifiable risk factors for cancer patients, as well as the initiation of cardioprotective medications. Despite recent advancements in precision cardio-oncology, knowledge gaps remain and limit our ability to appropriately predict with precision which patients will develop cardiovascular toxicity. Investigations using patient-specific CMs facilitate pharmacological discovery, mechanistic toxicity studies, and the identification of cardioprotective pathways. Studies with hiPSCs demonstrate that patients with comorbidities have more frequent adverse responses, compared to their counterparts without cardiac disease. Further studies utilizing hiPSC modeling should be considered, to evaluate the impact and mitigation of known cardiovascular risk factors, including blood pressure, body mass index (BMI), smoking status, diabetes, and physical activity in their role in cardiovascular toxicity after cancer therapy. Future real-world applications will depend on understanding the current use of hiPSC modeling in order for oncologists and cardiologists together to inform their potential to improve our clinical collaborative practice in cardio-oncology. When applying such in vitro characterization, it is hypothesized that a safety score can be assigned to each individual to determine who has a greater probability of developing cardiovascular toxicity. Using hiPSCs to create personalized models and ultimately evaluate the cardiovascular toxicity of individuals' treatments may one day lead to more patient-specific treatment plans in precision cardio-oncology while reducing cardiovascular disease (CVD) morbidity and mortality.
Subject(s)
Cardiovascular Diseases/etiology , Induced Pluripotent Stem Cells/cytology , Neoplasms/complications , Precision Medicine , Anthracyclines/toxicity , Cardiotoxicity , Cardiovascular Diseases/prevention & control , Cell Differentiation , Cellular Reprogramming , Humans , Receptor, ErbB-2/antagonists & inhibitors , Risk FactorsABSTRACT
BACKGROUND: As cardiovascular disease is a leading cause of death in cancer survivors, the new subspecialty of Cardio-Oncology has emerged to address prevention, monitoring, and management of cardiovascular toxicities to cancer therapies. During the coronavirus disease of 2019 (COVID-19) pandemic, we developed a Virtual-Hybrid Approach to build a de novo Cardio-Oncology Clinic. METHODS: We conceptualized a Virtual-Hybrid Approach including three arms: information seeking in locations with existing Cardio-Oncology clinics, information gathering at the location for a new clinic, and information sharing to report clinic-building outcomes. A retrospective review of outcomes included collection and synthesis of data from our first 3 months (at pandemic peak) on types of appointments, cancers, drugs, and cardiotoxicities. Data were presented using descriptive statistics. RESULTS: A de-novo Cardio-Oncology clinic was developed structured from the ground up to integrate virtual and in-person care in a hybrid and innovative model, using the three arms of the Virtual-Hybrid Approach. First, we garnered in-person and virtual preparation through hands-on experiences, training, and discussions in existing Cardio-Oncology Clinics and conferences. Next, we gleaned information through virtual inquiry and niche-building. With partners throughout the institution, a virtual referral process was established for outpatient referrals and inpatient e-consult referrals to actualize a hybrid care spectrum for our patients administered by a multidisciplinary hybrid care team of clinicians, ancillary support staff, and clinical pharmacists. Among the multi-subspecialty clinic sessions, approximately 50% were in Cardio-Oncology, 20% in Preventive Cardiology, and 30% in General Cardiology. In the hybrid model, the Heart & Vascular Center had started to re-open, allowing for 65% of our visits to be in person. In additional analyses, the most frequent cardiovascular diagnosis was cardiomyopathy (34%), the most common cancer drug leading to referral was trastuzumab (29%), and the most prevalent cancer type was breast cancer (42%). CONCLUSION: This Virtual-Hybrid Approach and retrospective review provides guidance and information regarding initiating a brand-new Cardio-Oncology Clinic during the pandemic for cancer patients/survivors. This report also furnishes virtual resources for patients, virtual tools for oncologists, cardiologists, and administrators tasked with starting new clinics during the pandemic, and innovative future directions for this digital pandemic to post-pandemic era.