The Role of Artificial Intelligence in Early Cancer Detection: A Primer on Artificial Intelligence

cancer remains a leading cause of morbidity and mortality worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. The increasing burden of cancer, driven by population growth and aging, necessitates innovative solutions to improve diagnosis, prognosis, and treatment outcomes. Artificial Intelligence has emerged as a transformative tool in oncology, offering significant potential in cancer detection, diagnosis, and personalized treatment strategies. This review explores the real-world applications of Artificial Intelligence in oncology, focusing on lung cancer and breast cancer, two of the most prevalent and deadly cancers globally. Artificial Intelligence-driven technologies, particularly in imaging, pathology, and genomics, have demonstrated remarkable success in enhancing early detection, diagnostic accuracy, and treatment planning. In lung cancer, Artificial Intelligence-powered imaging tools, such as deep learning models, have shown high sensitivity and specificity in detecting small pulmonary nodules, often missed by traditional methods. Similarly, in breast cancer, Artificial Intelligence has proven effective in mammography interpretation, reducing false positives and false negatives, and alleviating the workload of radiologists. Despite its promising potential, the integration of Artificial Intelligence into clinical practice faces several challenges, including issues related to data quality, algorithmic biases, and ethical considerations. The "black box" nature of many Artificial Intelligence systems poses a significant barrier to clinical acceptance, highlighting the need for explainable Artificial Intelligence to provide transparent and interpretable decision-making processes. Furthermore, the successful implementation of Artificial Intelligence in oncology requires robust regulatory frameworks and standardized protocols to ensure patient safety and data security. This review underscores the transformative potential of Artificial Intelligence in revolutionizing cancer care, emphasizing the importance of addressing key challenges to harness its full potential. By enhancing early detection, reducing diagnostic errors, and enabling personalized treatment strategies, Artificial Intelligence has the potential to significantly improve patient outcomes and reduce the global burden of cancer. However, its successful integration into clinical practice will depend on interdisciplinary collaboration, ethical considerations, and a commitment to responsible implementation.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

Artificial intelligence (AI) is revolutionizing various sectors, including healthcare, with significant advancements in early cancer detection. Traditional methods like biopsy and imaging, though widely used, have limitations in sensitivity and invasiveness. AI, with its ability to analyze vast datasets and identify complex patterns, offers new opportunities to enhance early diagnosis accuracy. This paper discusses AI techniques, including machine learning and deep learning, applied to cancer detection, highlighting their advantages, challenges, and potential. It also explores AI-driven case studies demonstrating significant improvements in screening accuracy and patient outcomes. The paper concludes by emphasizing the future role of AI in transforming cancer diagnostics and improving survival rates. Keywords: Artificial intelligence, early cancer detection, machine learning, deep learning, cancer diagnostics.

Lung cancer remains one of the deadliest cancers worldwide, with survival rates significantly dependent on early detection. Traditional diagnostic methods, while effective, often identify lung cancer in advanced stages, limiting treatment options. Recently, artificial intelligence (AI) and deep learning (DL) models have gained attention for their potential to assist in early lung cancer diagnosis through radiological data, particularly computed tomography (CT) and X-ray imaging. This review examines recent advancements in AI and DL models specifically applied to early lung cancer detection, offering a comprehensive look at model architecture, data preprocessing techniques, and performance metrics. AI models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown significant promise in improving diagnostic accuracy. In studies published over the past two years, CNN-based models have achieved sensitivity rates upwards of 90% when applied to large datasets, such as the LIDC-IDRI, a publicly available database containing thousands of annotated lung scans. Furthermore, hybrid approaches combining AI models with radiologist expertise have demonstrated reduced false positives, a frequent challenge in automated image analysis. A critical factor in these advancements has been data quality and volume. Large, annotated datasets with high-resolution images have enabled more robust training and validation, essential for refining these models. However, challenges remain, particularly regarding the standardization of data across institutions, variations in scanner quality, and ethical concerns surrounding patient privacy. The review also highlights studies exploring multi-modal approaches, integrating radiological data with clinical records and genetic markers to create more personalized diagnostic tools. These multi-modal methods have the potential to improve predictive accuracy further, though they currently require more extensive validation. Overall, while AI and DL technologies offer transformative potential for early lung cancer detection, widespread implementation depends on continued research in model accuracy, data standardization, and ethical safeguards. This paper concludes by emphasizing the need for collaboration across disciplines—AI researchers, radiologists, and oncologists—to refine these models into reliable tools that can be integrated seamlessly into clinical workflows for timely and accurate lung cancer diagnosis.

e16616 Background: Within the field of medicine, artificial intelligence (AI) is used to optimize diagnostic capacity, treatment planning, and prognostic evaluation. Research on AI has advanced significantly, but a comprehensive evaluation of the utility of AI in genitourinary (GU) oncology remains underexplored. We sought to conduct a bibliometric study of existing literature and a systematic review of randomized controlled trials (RCTs) to describe the state of the science regarding the use and applications of AI in GU oncology. Methods: We searched MEDLINE (Ovid), Embase (Ovid), and CINAHL Ultimate databases using search terms relevant to the concepts of GU oncology and AI. We excluded non-English papers, non-human studies, review articles, and articles using AI in manuscript writing. Our bibliometric study described articles from 2013-2023 using the term AI, and we categorized manuscripts by study type and cancer type. We also conducted a systematic review of RCTs assessing the use of AI in GU oncology. We used Covidence for screening and data extraction. Two authors independently reviewed all papers and Cochrane Risk of Bias (RoB) Tool 2.0 was used to assess for bias in the RCT studies. Results: The initial search identified 2,409 articles. After abstract review, 1,220 articles remained: 962 retrospective articles, 175 prospective studies, 79 with combined retrospective/prospective methods, and 4 RCTs. We also categorized studies by cancer type: 923 prostate, 274 renal, 194 urothelial, 8 testicular, and 2 penile cancers. AI-related articles grew exponentially from 14 in 2013 to 362 in 2023, with substantial growth starting in 2019 (92 that year). For our systematic review, we identified 4 RCTs: 1 in bladder cancer (BCa) and 3 in prostate cancer (PCa). Among the 4 RCTs, 2 focused on AI-based diagnostics, and the other 2 analyzed AI’s role in prognosis prediction and treatment planning. Of the diagnostic studies, 1 demonstrated that a neural network analyzing urinary biomarkers outperformed traditional methods in BCa diagnosis. The other demonstrated AI-enhanced imaging’s superior efficiency in detecting PCa. The third article demonstrated AI’s prognostic value in automated bone scan index for evaluating bone metastasis in PCa. The fourth study showed improved operational efficacy of AI-generated treatment plans compared to conventional brachytherapy planning in PCa. The 4 RCTs had varying levels of risk of bias, primarily due to the randomization process and deviations from intended interventions. Conclusions: Our bibliometric analysis of AI in GU oncology demonstrates the growing recognition of AI’s potential to enhance cancer care. Our systematic review identified four RCTs that highlight the diverse applications of AI and showcase AI’s ability in diagnostics and treatment planning. Collectively, this work provides information about the promise of AI in oncology while improving clinical outcomes.

Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. The complexity and heterogeneity of IRDs pose significant challenges in diagnosis, prognosis, and management. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. However, the rapid development of AI techniques and their varied applications have led to fragmented knowledge in this field. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs. It aims to structure pathways for advancing clinical applications by exploring AI techniques like machine learning and deep learning, particularly in disease detection, progression prediction, and personalized treatment planning. Additionally, the integration of explainable AI is discussed, emphasizing its importance in clinical settings to improve transparency and trust in AI-based systems. The review addresses the need to bridge existing gaps in focused studies on AI's role in IRDs, offering a structured analysis of current AI techniques and outlining future research directions. It concludes with an overview of the challenges and opportunities in deploying AI for IRDs, highlighting the need for interdisciplinary collaboration and the continuous development of robust, interpretable AI models to advance clinical applications.

Breast cancer remains the most prevalent cancer among women globally, imposing significant economic and human burdens. Early detection is pivotal for improving patient outcomes and reducing treatment costs. This review explores the transformative role of artificial intelligence (AI) in the early detection of breast cancer, highlighting its integration into various aspects of health care. AI, particularly through ML and DL algorithms, enhances the accuracy and efficiency of imaging modalities such as mammography, ultrasound, and magnetic resonance imaging by identifying subtle abnormalities and personalizing risk assessments. Advanced AI models such as convolutional neural networks (CNNs) and vision transformers (ViTs) have demonstrated superior performance in analyzing complex medical images, leading to earlier diagnoses and reduced false positives and negatives. Additionally, AI-driven liquid biopsies offer minimally invasive alternatives for detecting circulating tumor DNA and other biomarkers, further advancing early detection capabilities. The review also addresses the diverse data types utilized in breast oncology, including electronic health records, digital pathology, radiomics, genomics, proteomics, and metabolomics, emphasizing the potential of multi-omic approaches for comprehensive cancer profiling. However, the clinical integration of AI faces challenges such as data quality, interoperability, workflow alignment, and ethical concerns including bias and data privacy. Future directions include the development of explainable AI models, digital twin technologies, implementation beyond detecting including clinical decision support, and the incorporation of multi-modal data to enhance diagnostic precision and personalized medicine. By addressing these challenges through multi-disciplinary collaboration, AI holds the promise of revolutionizing early breast cancer detection, ultimately improving patient outcomes and reducing the global burden of this pervasive disease.

Objective : To analyze foreign experience in the use of artificial intelligence (AI) in oncology, as well as examine current advances in AI and its impact on clinical practice. Material and methods. A systematic review of foreign literature was carried out, current AI programs and technologies in oncology were analyzed. The total number of identified records via the PubMed/MEDLINE search was 7680. After publication selection in accordance with the PRISMA guidelines, 32 studies that met all criteria were randomly included in the review and formed the basis for the analysis. Results . AI demonstrates high efficiency in cancer diagnostics, including early detection of tumors, analysis of medical images and pathological data. A literature review on the use of AI models in oncology revealed exponential growth from 2010 to 2022, confirming the active development of the field. Diagnostic and treatment reports generated by the AI technologies indicated a comparable level of accuracy to that of experienced oncologists; they also revealed the ability to improve clinical outcomes. Furthermore, the introduction of AI stimulates the development of personalized treatment, increased patient adherence to therapy and optimization of the work of medical organizations. AI’s impact was revealed on physicians (reduced diagnostic and treatment errors), patients (personalized support), and hospitals (smart management systems). Conclusion . AI is becoming an integral part of modern oncology, offering new opportunities to improve diagnostics, treatment, outcome prediction, and patient support. A study of the dynamics of publications and AI models indicates that the use of AI in clinical oncology is rapidly developing, opening up new prospects for the creation of smart hospitals, simplification of medical data exchange, development of personalized medicine, and improvement of the quality of patient care. The integration of AI with emerging technologies such as wearable devices and multimodal analysis promises to revolutionize oncology practice.

Artificial intelligence (AI) is increasingly transforming intensive care medicine by enabling advanced analysis of complex clinical data generated in intensive care units (ICUs). This review explores current and emerging applications of AI in ICU practice, including sepsis prediction, mechanical ventilation management, acute kidney injury (AKI) forecasting, haemodynamic monitoring, and prognostication. AI-based models have demonstrated the ability to improve early detection of complications, support clinical decision-making, and optimise resource utilisation. However, challenges such as limited interpretability, data integration constraints, and the need for prospective validation continue to hinder widespread clinical adoption. A comprehensive narrative review was conducted using publications from January 2015 to June 2025. Combinations of the terms "artificial intelligence", "machine learning", "deep learning", "intensive care unit", "critical care", "clinical decision support", and "sepsis prediction" were used to search PubMed, Scopus, and Google Scholar. Peer-reviewed original research, systematic reviews, and meta-analyses reporting on the practical uses or clinical validation of AI tools in ICUs were given precedence, while studies focusing solely on algorithm development without clinical integration were excluded. Sepsis, mechanical ventilation, AKI, haemodynamic monitoring, and prognostication are among the thematic areas of application that organise the review. AI has shown significant utility across ICU domains, including early prediction of complications, forecasting mechanical ventilation duration, risk stratification, haemodynamic instability alerts, and mortality prognostication. Models trained on real-world ICU datasets have demonstrated high predictive accuracy and potential for early intervention. However, challenges such as model interpretability, data fragmentation, and ethical concerns remain.

Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology. We utilized PubMed to search for peer-reviewed research articles published between 2016 and 2021 with the query type "("deep learning" or "machine learning" or "neural network" or "artificial intelligence") and ("neoplas$" or "cancer$" or "tumor$" or "tumour$")." We included clinical trials and original research studies and excluded reviews and meta-analyses. Oncology-related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies. Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%-93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. "White" vs. "non-White" or "White" vs. "Black"). We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non-White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

This chapter explores the transformative impact of artificial intelligence (AI) on leadership practices in the modern era. It begins with an overview of AI's rapid evolution and its pervasive influence across industries, highlighting its role in reshaping organizational dynamics and fostering a data-driven culture. The concept of AI-driven leadership is defined, emphasizing its distinctive characteristics such as data-centricity, agility, and enhanced decision-making capabilities compared to traditional leadership models. Key topics include AI's role in strategic decision-making, innovation acceleration, and organizational agility. Ethical considerations surrounding AI applications, workforce implications, and future trends are also discussed, underscoring AI's pivotal role in shaping next-generation leadership strategies and organizational success.

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer's disease, Parkinson's diseases, COVID-19, and diabetes. The author keyword analysis identified "deep learning", "convolutional neural network", and "classification" as dominant research themes. Conclusions: AI's transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.

Simple SummaryThe combination of digital pathology (DP) with artificial intelligence (AI) offers faster, more accurate, and more comprehensive diagnoses, resulting in more precise individualized treatment. As this technology is constantly evolving, it is critical to understand the current state of AI applications in DP. Thus, it is necessary to analyze AI patent applications, assignees, and leaders in the field. In this study, five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, were searched using key phrases, such as DP, AI, machine learning, and deep learning, and 523 patents were shortlisted based on the inclusion criteria. Our data demonstrated that the key areas of the patents were whole-slide imaging, segmentation, classification, and detection. In the past five years, an increasing trend in patent filing has been observed, mainly in a few prominent countries, with a focus on the digitization of pathological images and AI technologies that support the critical role of pathologists.The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.

Background: Artificial intelligence (AI) is transforming healthcare systems by improving operational efficiency, simplifying patient care procedures, and improving diagnostic accuracy. Artificial intelligence (AI) technologies, like machine learning and natural language processing, present previously unheard-of chances to quickly and accurately evaluate enormous volumes of healthcare data, assisting with clinical decision-making and enhancing patient outcomes. Aim thorough examination and analysis of artificial intelligence's impact on healthcare leadership, with a particular emphasis on present nursing trends and their implications for the future. The study tries to uncover the advantages, difficulties, and consequences of AI integration by looking at how AI technologies including clinical decision support systems, predictive analytics, robots, natural language processing, and telehealth are being used in nursing practice. Method: A comprehensive analysis including research articles published between 2015 and 2024 was carried out. To give a thorough overview of AI's present and future uses in healthcare, major themes and trends were found and summarized. Results: By stressing AI's role in improving diagnostic accuracy and patient outcomes, the study highlights the technology's major contributions to drug discovery, virtual patient care, and medical imaging. Human-centered design concerns, the necessity of educational changes, and ethical challenges surrounding the application of AI surfaced as crucial topics needing attention. Conclusion: AI has enormous potential to transform healthcare by enhancing operational effectiveness, optimizing the delivery of care, and increasing diagnostic precision. Still, ethical issues must be resolved, interdisciplinary cooperation must be promoted, and educational frameworks must be improved in order to provide healthcare workers with the necessary AI skills.