The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review

This systematic review investigates the advancements and challenges of artificial intelligence (AI) in precision oncology, focusing on research from 2021 to 2024, to provide an evidence-based roadmap for future implementation. Following the PRISMA guidelines, a comprehensive search was conducted across Scopus, SciELO, and Google Scholar using relevant keywords to identify studies evaluating AI applications in cancer diagnosis and treatment. Eighteen relevant articles were selected and qualitatively analyzed to identify key themes and patterns. AI models, including machine learning and deep learning, have demonstrated significant improvements in diagnostic accuracy, treatment planning, and personalized therapies. Examples include a hybrid CatBoost-MLP model that achieved 98.06% accuracy in breast tissue classification and a deep convolutional neural network with 92.08% sensitivity for early gastric cancer detection. AI also reduces radiotherapy planning times, enhancing accessibility, particularly in developing countries. The integration of AI into oncology has transformative potential, enhancing diagnostic precision, risk stratification, and personalized treatment strategies. However, challenges remain, including data standardization, the need for diverse datasets, and ethical considerations. This study highlights the need for robust AI models, international data standards, and ethical frameworks to ensure the safe, equitable, and effective clinical implementation of AI in oncology, paving the way for improved patient outcomes and healthcare accessibility.

With the evolution of Artificial Intelligence (AI), even cancer care approaches are evolving as it is providing innovative solutions to some of the most complex challenges in oncology. This article delves into how AI is making a profound impact across the cancer care spectrum worldwide, from early detection and precise diagnosis to the personalization of treatment and improved patient management. By harnessing AI's ability to analyze massive datasets and identify patterns beyond human perception, healthcare professionals can offer more accurate diagnoses and more effective treatments tailored to individual patient needs. This review also highlights the most recent advancements in AI-driven technologies in oncology and looks toward the future, where AI's role is expected to expand further. By discussing the potential and challenges of AI in cancer care, this article offers insights into how it is reshaping oncology practice, with the ultimate goal of enhancing patient outcomes and revolutionizing cancer treatment. This article aims to explore the transformative role of Artificial Intelligence (AI) in oncology, focusing on its impact on early cancer detection, precise diagnosis, personalized treatment, and overall patient management. It seeks to provide insights into the recent advancements of AI in cancer care, the challenges associated with its integration, and the potential future directions in oncology.A comprehensive review of literature was conducted, focusing on AI applications in oncology, including diagnostic imaging, precision oncology, and clinical decision support systems. Recent studies were analyzed to understand the role of AI-driven technologies in cancer diagnosis, treatment, and management. Inclusion criteria: Peer-reviewed articles, case studies, and reviews published in the last five years that focus on the application of AI in oncology, including early cancer detection, diagnostic accuracy, personalized treatment, and clinical decision support systems. Exclusion criteria: Articles that did not focus on oncology, did not involve AI technologies, or were not peer-reviewed were excluded from this review. AI has shown significant improvements in cancer detection and diagnostic accuracy, particularly through advanced imaging techniques and personalized treatment strategies. AI-powered diagnostic tools have revolutionized imaging by enhancing detection rates and reducing diagnostic errors. Moreover, AI has played a crucial role in tailoring therapeutic interventions based on individual patient characteristics, thus contributing to precision oncology. AI is revolutionizing cancer care by improving diagnostic precision, personalizing treatments, and enhancing patient outcomes. However, challenges such as data privacy, algorithm bias, and regulatory complexities must be addressed. Future innovations in AI, along with collaborative efforts, will further enhance cancer care and pave the way for AI-driven oncology practices globally.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

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.

Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. To evaluate oncologists' views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan & Provider Enumeration System. The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

The past decade has seen major advances in the use of artificial intelligence (AI) to solve various biomedical problems, including cancer. This has resulted in more than 6000 scientific papers focusing on AI in oncology alone. The expansiveness of this research area presents a challenge to those seeking to understand how it has developed. A scientific analysis of AI in the oncology literature is therefore crucial for understanding its overall structure and development. This may be addressed through bibliometric analysis, which employs computational and visual tools to identify research activity, relationships, and expertise within large collections of bibliographic data. There is already a large volume of research data regarding the development of AI applications in cancer research. However, there is no published bibliometric analysis of this topic that offers comprehensive insights into publication growth, co-citation networks, research collaboration, and keyword co-occurrence analysis for technological trends involving AI across the entire spectrum of oncology research. The purpose of this study is to investigate documents published during the last decade using bibliometric indicators and network visualization. This will provide a detailed assessment of global research activities, key themes, and AI trends over the entire breadth of the oncology field. It will also specifically highlight top-performing authors, organizations, and nations that have made major contributions to this research domain, as well as their interactions via network collaboration maps and betweenness centrality metric. This study represents the first global investigation of AI covering the entire cancer field and using several validated bibliometric techniques. It should provide valuable reference material for reorienting this field and for identifying research trajectories, topics, major publications, and influential entities including scholars, institutions, and countries. It will also identify international collaborations at three levels: micro (that of an individual researcher), meso (that of an institution), and macro (that of a country), in order to inform future lines of research. The Science Citation Index Expanded from the Web of Science Core Collection was searched for articles and reviews pertaining exclusively to AI in cancer from 2012 through 2022. Annual publication trends were plotted using Microsoft Excel 2019. CiteSpace and VOSViewer were used to investigate the most productive countries, researchers, journals, as well as the sharing of resources, intellectual property, and knowledge base in this field, along with the co-citation analysis of references and keywords. A total of 6757 documents were retrieved. China produced the most publications of any country (2087, 30.89%), and Sun Yat Sen University the highest number (167, 2.47%) of any institute. WEI WANG was the most prolific author (33, 0.49%). RUI ZHANG ranked first for highest betweenness centrality (0.21) and collaboration criteria. Scientific Reports was found to be the most prolific journal (208, 3.18%), while PloS one had the most co-citations (2121, 1.55%). Strong and ongoing citation bursts were found for keywords such as "tissue microarray", "tissue segmentation", and "artificial neural network". Deep learning currently represents one of the most cutting-edge and applicable branches of AI in oncology. The literature to date has dealt extensively with radiomics, genomics, pathology, risk stratification, lesion detection, and therapy response. Current hot topics identified by our analysis highlight the potential application of AI in radiomics and precision oncology.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The aim of the article is to highlight the key role of artificial intelligence in modern oncology. The search for scientific publications was carried out through the following web search engines: PubMed, PMC, Web of Science, Scopus, Embase and Ebsco. Artificial intelligence plays a special role in oncology and is considered to be the future of oncology. The largest application of artificial intelligence in oncology is in diagnostics (more than 80%), particularly in radiology and pathology. This can help oncologists not only detect cancer at an early stage but also forecast the possible development of the disease by using predictive models. Artificial intelligence plays a special role in clinical trials. AI makes it possible to accelerate the discovery and development of new drugs, even if not necessarily successfully. This is done by detecting new molecules. Artificial intelligence enables patient recruitment by combining diverse demographic and medical patient data to match the requirements of a given research protocol. This can be done by reducing population heterogeneity, or by prognostic and predictive enrichment. The effectiveness of artificial intelligence in oncology depends on the continuous learning of the system based on large amounts of new data but the development of artificial intelligence also requires the resolution of some ethical and legal issues.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

The Impact of Artificial Intelligence on Health Equity in Oncology: A Scoping Review

Revolutionizing Digital Pathology with the Power of Generative Artificial Intelligence and Foundation Models

Autonomous vehicles are reshaping the car rental and ridesharing industries, potentially leading to a unified model of on-demand transportation suitable for both uncommon (e.g., business trips) and daily commuting. An exploratory study of human behavior towards autonomous vehicles can uncover the challenges and opportunities inherent in different levels of vehicle automation. This study aims to (a) identify behavioral differences in drivers operating vehicles at various levels of automation and (b) explore how these behaviors vary with different assistance feature styles, specifically between risky and conservative modes. Human-subject experiments were conducted among twelve participants (aged 21 to 29, including four women) to complete simulated driving trials under different levels of automation (Levels 0, 3, and 5), assistance features (risky and conservative modes), and driving activities (lane keeping and lane changing). Measures of driving performance, body posture, and eye movement were recorded during each trial. The data implied that: (1) driving performance: drivers exhibited stable speed and steering control at Levels 0 and 5, while speed decreased and steering variability increased obviously at Level 3; (2) driving posture: a tense posture was noted at Level 0, with potential posture preparation needed for takeover actions at Level 3; (3) eye movement: active scanning and continuous control were maintained at Level 0, with notable shifts in attention at Levels 3 and 5. Further research could focus on conducting on-road tests, using equipment designed for on-road tests and broadening the demographic range of participants.

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers. The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package "Bibliometrix" was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences. A total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were "model" and "human papillomavirus." The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates. By analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field.