Artificial Intelligence In Clinical Practice Research Articles

Artificial intelligence in clinical practice: Quality and evidence

A revolution is taking place within the field of artificial intelligence (AI) with the emergence of generative AI. Although we are in an early phase at the clinical level, there is an exponential increase in the number of scientific articles that use AI (discriminative and generative) in their methodology. According to the current situation, we may be in an “AI bubble” stage; requiring filters and tools to evaluate its application, based on the quality and evidence provided. In this sense, initiatives have been developed to determine standards and guidelines for the use of discriminative AI (CONSORT AI, STARD AI and others), and more recently for generative AI (the CHART collaborative). As a new technology, AI requires scientific regulation to guarantee the efficacy and safety of its applications, while maintaining the quality of care; an evidence-based AI (IABE).

Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities

Numerous cancer therapies have detrimental cardiovascular effects on cancer survivors. Cardiovascular toxicity can span the course of cancer treatment and is influenced by several factors. To mitigate these risks, cardio-oncology has evolved, with an emphasis on prevention and treatment of cardiovascular complications resulting from the presence of cancer and cancer therapy. Artificial intelligence (AI) holds multifaceted potential to enhance cardio-oncologic outcomes. AI algorithms are currently utilizing clinical data input to identify patients at risk for cardiac complications. Additional application opportunities for AI in cardio-oncology involve multimodal cardiovascular imaging, where algorithms can also utilize imaging input to generate predictive risk profiles for cancer patients. The impact of AI extends to digital health tools, playing a pivotal role in the development of digital platforms and wearable technologies. Multidisciplinary teams have been formed to implement and evaluate the efficacy of these technologies, assessing AI-driven clinical decision support tools. Other avenues similarly support practical application of AI in clinical practice, such as incorporation into electronic health records (EHRs) to detect patients at risk for cardiovascular diseases. While these AI applications may help improve preventive measures and facilitate tailored treatment to patients, they are also capable of perpetuating and exacerbating healthcare disparities, if trained on limited, homogenous datasets. However, if trained and operated appropriately, AI holds substantial promise in positively influencing clinical practice in cardio-oncology. In this review, we explore the impact of AI on cardio-oncology care, particularly regarding predicting cardiotoxicity from cancer treatments, while addressing racial and ethnic biases in algorithmic implementation.

How Explainable Artificial Intelligence Can Increase or Decrease Clinicians' Trust in AI Applications in Health Care: Systematic Review.

Artificial intelligence (AI) has significant potential in clinical practice. However, its "black box" nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians' trust in AI-driven clinical decision-making. A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians' trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians' trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians' trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice.

Evaluating the Effectiveness of a Large Language Model in the Psychological Assessment of Potential Liver Transplant Candidates: A Feasibility Study

Introduction: The use of artificial intelligence (AI) in medical settings has shown promise in various domains including diagnostics, treatment recommendation, and patient management. Recent advances have explored the potential of AI in pre-surgical assessments, but its application in transplant psychology remains unexplored. The objective of this study is to assess the effectiveness of AI in the context of screening potential liver transplant donors and recipients. To assess the feasibility of using ChatGPT-4 to screen potential liver transplant donors and recipients. Methods: This study utilizes a cross-sectional research design to evaluate the feasibility of using ChatGPT-4 in the preliminary screening of living liver donors and liver transplant candidates. The study aims to determine the accuracy and reliability of ChatGPT-4 in assessing hypothetical scenarios involving potential donors and recipients. Results: The analysis showed no found no significant differences between ChatGPT-4 and the expert panel in assessing liver transplant candidates, demonstrating an overall accuracy of 83.58%, sensitivity of 56.10%, and specificity of 80.49%. Additionally, the Cohen’s Kappa statistic of 0.68 (95% CI: 0.52-0.83) indicated substantial agreement between ChatGPT-4 and the psychologists’ evaluations. The absence of false positives (0%) and a low false negative rate (8%) emphasize ChatGPT-4's cautious and accurate decision-making capabilities. Conclusion: The findings of this study demonstrate that ChatGPT-4 has the potential to serve as an effective screening tool for liver transplant candidates, complementing the work of human experts and enhancing the overall efficiency of the transplant process. While challenges remain, the integration of AI into the liver transplantation workflow could lead to significant improvements in candidate evaluation and patient outcomes, paving the way for the broader application of AI in clinical practice.

Generative Artificial Intelligence in Clinical Practice: Undergraduate Experience

The objective of this article is to assess the impact of generative artificial intelligence (AGI) as a learning tool in clinical practice, as perceived by clinical students of human medicine. To this end, six learning activities were devised and executed, employing diverse pedagogical approaches and AGI tools, with the objective of addressing various facets of clinical practice. These included the creation of explanatory material, literature analysis, the selection of clinical cases for publication, the development of self-assessment questions, the production of explanatory videos, and the creation of scientific posters. These activities were conducted during the clinical internships of the fifth year of the medical curriculum and were evaluated using both quantitative and qualitative methods. The findings indicated that the incorporation of GAI as a pedagogical instrument in the clinical training of medical students yielded a favorable influence on their motivation, self-assurance, satisfaction, competencies, and knowledge. The students identified GAI as a novel, applicable, useful, and transferable tool and expressed interest in continuing to use it in the future. However, the results also indicated that students encountered certain challenges and difficulties in utilizing GAI as a learning tool, including overconfidence, resistance, and a lack of understanding. In conclusion, this study provides empirical evidence on the use of GAI as a learning tool for undergraduate medical students' clinical practices, and contributes to the knowledge base regarding the possibilities and challenges of GAI application in higher education.

A review of artificial intelligence applications in in vitro fertilization.

The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the efficiency of diagnosing and treating reproductive disorders. The integration of AI algorithms into the in vitro fertilization (IVF) has the potential to represent the next frontier in advancing personalized reproductive medicine and enhancing fertility outcomes for patients. The potential of AI lies in its ability to bring about a new era characterized by standardization, automation, and an improved success rate in IVF. At present, the utilization of AI in clinical practice is still in its early stages and faces numerous ethical, regulatory, and technical challenges that require attention. In this review, we present an overview of the latest advancements in various applications of AI in IVF, including follicular monitoring, oocyte assessment, embryo selection, and pregnancy outcome prediction. The aim is to reveal the current state of AI applications in the field of IVF, their limitations, and prospects for future development. Further studies, which involve the development of comprehensive models encompassing multiple functions and the conduct of large-scale randomized controlled trials, could potentially indicate the future direction of AI advancements in the field of IVF.

Evaluation of the Impact of Artificial Intelligence on Clinical Practice of Radiology in Saudi Arabia.

Artificial Intelligence (AI) is becoming integral to the health sector, particularly radiology, because it enhances diagnostic accuracy and optimizes patient care. This study aims to assess the awareness and acceptance of AI among radiology professionals in Saudi Arabia, identifying the educational and training needs to bridge knowledge gaps and enhance AI-related competencies. This cross-sectional observational study surveyed radiology professionals across various hospitals in Saudi Arabia. Participants were recruited through multiple channels, including direct invitations, emails, social media, and professional societies. The survey comprised four sections: demographic details, perceptions of AI, knowledge about AI, and willingness to adopt AI in clinical practice. Out of 374 radiology professionals surveyed, 45.2% acknowledged AI's significant impact on their field. Approximately 44% showed enthusiasm for AI adoption. However, 58.6% reported limited AI knowledge and inadequate training, with 43.6% identifying skill development and the complexity of AI educational programs as major barriers to implementation. While radiology professionals in Saudi Arabia are generally positive about integrating AI into clinical practice, significant gaps in knowledge and training need to be addressed. Tailored educational programs are essential to fully leverage AI's potential in improving medical imaging practices and patient care outcomes.

AI sees beyond humans: automated diagnosis of myopia based on peripheral refraction map using interpretable deep learning

The question of whether artificial intelligence (AI) can surpass human capabilities is crucial in the application of AI in clinical medicine. To explore this, an interpretable deep learning (DL) model was developed to assess myopia status using retinal refraction maps obtained with a novel peripheral refractor. The DL model demonstrated promising performance, achieving an AUC of 0.9074 (95% CI 0.83–0.97), an accuracy of 0.8140 (95% CI 0.70–0.93), a sensitivity of 0.7500 (95% CI 0.51–0.90), and a specificity of 0.8519 (95% CI 0.68–0.94). Grad-CAM analysis provided interpretable visualization of the attention of DL model and revealed that the DL model utilized information from the central retina, similar to human readers. Additionally, the model considered information from vertical regions across the central retina, which human readers had overlooked. This finding suggests that AI can indeed surpass human capabilities, bolstering our confidence in the use of AI in clinical practice, especially in new scenarios where prior human knowledge is limited.

Application of Artificial Intelligence in Clinical Practice - Perception of a Multinational Group of Nephrologists.

This study investigates the perception of a multinational group of nephrologists on artificial intelligence (AI) application in clinical practice. A validated on-line survey was performed in March 2024, in 4 continents. The results revealed a prevalent familiarity with AI and machine learning (ML) terms, but traditional tools remained favored for clinical decision support. AI's future relevance was acknowledged by more than two thirds of the sample but concerns related to the use of this tool in clinical practice were shared, particularly by nephrologists without any previous contact with AI. This reinforces the need for education in this group of health professionals, to allow full adoption of AI in the management of chronic kidney disease (CKD) in the near future.

Artificial Intelligence in Clinical Practice

Artificial Intelligence in Clinical Practice

Revolutionizing Healthcare: The Role of Artificial Intelligence in Clinical Practice

Revolutionizing Healthcare: The Role of Artificial Intelligence in Clinical Practice

Artificial intelligence and nonoperating room anesthesia.

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Artificial Intelligence in Obstetrics and Gynecology: Transforming Care and Outcomes.

The integration of artificial intelligence (AI) in obstetrics and gynecology (OB/GYN) is revolutionizing the landscape of women's healthcare. This review article explores the transformative impact of AI technologies on the diagnosis, treatment, and management of obstetric and gynecological conditions. We examine key advancements in AI-driven imaging techniques, predictive analytics, and personalized medicine, highlighting their roles in enhancing prenatal care, improving maternal and fetal outcomes, and optimizing gynecological interventions. The article also addresses the challenges and ethical considerations associated with the implementation of AI in clinical practice. This paper highlights the potential of AI to greatly improve the standard of care in OB/GYN, ultimately leading to better health outcomes for women, by offering a thorough overview of present AI uses and future prospects.

Making sense of artificial intelligence and large language models-including ChatGPT-in pediatric hematology/oncology.

ChatGPT and other artificial intelligence (AI) systems have captivated the attention of healthcare providers and researchers for their potential to improve care processes and outcomes. While these technologies hold promise to automate processes, increase efficiency, and reduce cognitive burden, their use also carries risks. In this commentary, we review basic concepts of AI, outline some of the capabilities and limitations of currently available tools, discuss current and future applications in pediatric hematology/oncology, and provide an evaluation and implementation framework that can be used by pediatric hematologist/oncologists considering the use of AI in clinical practice.

Revolutionizing dermatology: The role of artificial intelligence in clinical practice

Revolutionizing dermatology: The role of artificial intelligence in clinical practice

Teleneurology and Artificial Intelligence in Clinical Practice.

As teleheath becomes integrated into the practice of medicine, it is important to understand the benefits, limitations, and variety of applications. Telestroke was an early example of teleneurology that arose from a need for urgent access to neurologists for time-sensitive treatments for stroke. It made a scarce resource widely available via video conferencing technologies. Additionally, applications such as outpatient video visits, electronic consultation (e-consult), and wearable devices developed in neurology, as well. Telehealth dramatically increased during the COVID-19 pandemic when offices were closed and hospitals were overwhelmed; a multitude of both outpatient and inpatient programs developed and matured during this time. It is helpful to explore what has been learned regarding the quality of telehealth, disparities in care, and how artificial intelligence can interact with medical practices in the teleneurology context.

Искусственный интеллект в медицине: этические проблемы коммуникации между врачом и пациентом

A serious challenge to the patient-oriented model of interaction between doctor and patient is posed by objective circum-stances: the constantly increasing number of patients and rising healthcare costs and, therefore, the inability to provide the attention and care necessary for the patient. A likely response to the emerging "crisis of care" in medicine appears to be the use of digital technolo-gies in clinical practice and healthcare organization. In particular, one of the possible options for using the potential of digital technolo-gies in medicine is artificial intelligence systems (hereinafter referred to as AI).The article is devoted to the peculiarities of communica-tion between a doctor and patients in the conditions of using artificial intelligence in clinical practice. The author identifies both the prospects for the introduction of artificial intelligence and difficulties in the process of communication between a doctor and a patient. The purpose of the article is to analyze the mechanism of influence of artificial intelligence on the process of communication between a doctor and a patient, to assess the impact of digital technologies on the basic values that develop in the process of clinical communica-tion – respect for patient autonomy, trust, confidentiality. The main expected positive consequence of the use of AI in medicine is the freeing up of time for direct communication with the patient. However, there is a risk that it will be used to increase patient flow, espe-cially in the commercial medical sector. It is concluded that, despite the widespread use of digital technologies, society remains wary of such innovations, especially when it concerns personal data. As a result, the level of trust and willingness to use AI systems is low. When introducing AI, it is necessary to take into account the feasibility and appropriateness of using these technologies in the provision of medical care, since, along with the effectiveness and accuracy of diagnostic results and treatment, the process of communication with a doctor remains extremely important for the patient, the opportunity to share their medical history and, during this communica-tion, form trust necessary for joint decision-making.

Artificial intelligence in diagnosis of neurodegenerative disorders (literature review)

Currently, there are virtually no objective diagnostic signs (markers) to diagnose neurodegenerative diseases, that is why it may take months of constant monitoring of symptoms to establish a reliable diagnosis. Now there is an increasing number of reports that artificial intelligence can provide a more accurate diagnosis of neurodegenerative diseases by identifying specific diagnostic features from electroencephalography data, neuroimaging, and wearable devices and smartphones. This study reviews the application of artificial intelligence to the diagnosis of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. Using the international databases Scopus and PubMed, as well as the Russian Science Citation Index, we analysed studies that used artificial intelligence methods to detect, monitor, or control the progression of neurodegenerative diseases. The primary focus is on analysing electroencephalography data, neuroimaging data, and data from wearable devices and smartphones. The use of the latter may allow screening, diagnosis, and monitoring of the disease at home with a minimum economic cost. Artificial intelligence can be a useful tool for early, accurate, and non-invasive diagnosis of neurodegenerative diseases, as well as for assessing the effectiveness of treatment and predicting the course of the disease. However, for the widespread implementation of artificial intelligence in clinical practice, several issues related to the quality, availability, and standardization of data, validation and interpretation of results, ethical and legal aspects need to be resolved. The use of artificial intelligence requires both specialists from the IT industry with a deep understanding of the types and kinds of neural networks and physicians with fundamental knowledge of neurology, psychiatry, molecular biology, and biophysics.

Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine

ObjectivesTo explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units...

Artificial Intelligence in Healthcare: A Study of Physician Attitudes and Perceptions in Jeddah, Saudi Arabia.

Background Artificial intelligence (AI) in healthcare is rapidly advancing, reshaping diagnostic, prognostic, and operational tasks in healthcare institutions. The adoption of AI among physicians is varied, with concerns over job loss, medical errors, and lack of emotional intelligence. This study aimed to assess physicians' attitudes and perceptions toward AI in clinical practice in Jeddah, Saudi Arabia, and the factors affecting these attitudes and perceptions. Methodology A cross-sectional study was conducted among physicians at two major hospitals in Jeddah. An in-person digital survey consisted of questions regarding demographic characteristics, attitudes toward clinical AI, and perceptions of AI's impact on healthcare. Results Of the 205 participants, 76% agreed on the accuracy of AI systems, and 60% acknowledged their efficiency as a factor that could influence their willingness to use clinical AI. However, only 25.9% reported using these systems in the past year, with the majority, 74.1%, indicating they had never used them. Notably, there was a significant association between gender and attitude toward AI, with males being more likely to have a positive attitude (p = 0.01). Conclusions While the majority of participants recognized the potential benefits of AI in healthcare, its actual utilization was low. The findings suggest the need for increased AI-related training and education among physicians and the fostering of collaboration between computer scientists, engineers, and medical professionals to accelerate the development of clinically relevant AI tools.