Artificial Intelligence In Oncology Research Articles

Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022

PurposeOncology is the primary field in medicine with a high rate of artificial intelligence (AI) use. Thus, this study aimed to investigate the trends of AI in oncology, evaluating the bibliographic characteristics of articles. We evaluated the related research on the knowledge framework of Artificial Intelligence (AI) applications in Oncology through bibliometrics analysis and explored the research hotspots and current status from 1992 to 2022.MethodsThe research employed a scientometric methodology and leveraged scientific visualization tools such as Bibliometrix R Package Software, VOSviewer, and Litmaps for comprehensive data analysis. Scientific AI-related publications in oncology were retrieved from the Web of Science (WoS) and InCites from 1992 to 2022.ResultsA total of 7,815 articles authored by 35,098 authors and published in 1,492 journals were included in the final analysis. The most prolific authors were Esteva A (citaition = 5,821) and Gillies RJ (citaition = 4288). The most active institutions were the Chinese Academy of Science and Harward University. The leading journals were Frontiers ın Oncology and Scientific Reports. The most Frequent Author Keywords are "machine learning", "deep learning," "radiomics", "breast cancer", “melanoma” and "artificial intelligence," which are the research hotspots in this field. A total of 10,866 Authors' keywords were investigated. The average number of citations per document is 23. After 2015, the number of publications proliferated.ConclusionThe investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment. This bibliometric analysis offered valuable perspectives into AI's role in Oncology research, shedding light on emerging research paths. Notably, a significant portion of these publications originated from developed nations. These findings could prove beneficial for both researchers and policymakers seeking to navigate this field.

Contextualising legal and ethical conundrums of artificial intelligence in oncology

Contextualising legal and ethical conundrums of artificial intelligence in oncology

Towards equitable AI in oncology.

Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.

Integrating AI-driven Wearable Technology in Oncology Decision Making: A Narrative Review.

Clinical decision-making in oncology is a complex process influenced by numerous disease-related factors, patient demographics, and logistical considerations. With the advent of Artificial Intelligence (AI), precision medicine is undergoing a shift towards more precise and personalized care. Wearable device technology complements this paradigm shift by offering continuous monitoring of patient vitals, facilitating early intervention, and improving treatment adherence. The integration of these technologies promises to enhance the quality of oncological care, making it more responsive and tailored to individual patient needs, thereby enabling wider implementation of such applications in the clinical setting. This review article addresses the integration of wearable devices and AI in oncology, exploring their role in patient monitoring, treatment optimization, and research advancement along with an overview of completed clinical trials and utility in different aspects. The vast applications have been exemplified using several studies and all the clinical trials completed till date have been summarized in table 2. Additionally, we discuss challenges in implementation, regulatory considerations, and future perspectives for leveraging these technologies to enhance cancer care and radically changing the global health sector. AI is transforming cancer care by enhancing diagnostic, prognostic, and treatment planning tools, thus making precision medicine more effective. Wearable technology facilitates continuous, non-invasive monitoring, improving patient engagement and adherence to treatment protocols. The combined use of AI and wearables aids in monitoring patient activity, assessing frailty, predicting chemotherapy tolerance, detecting biomarkers, and managing treatment adherence. Despite these advancements, challenges, such as data security, privacy, and the need for standardized devices persist. In the foreseeable future, wearable technology can hold significant potential to revolutionize personalized oncology care, empowering clinicians to deliver comprehensive and tailored treatments alongside standard therapy.

"The Role of Artificial Intelligence in Oncology: Transforming Cancer Diagnosis and Treatment"

"The Role of Artificial Intelligence in Oncology: Transforming Cancer Diagnosis and Treatment"

Empowering health care consumers & understanding patients' perspectives on AI integration in oncology and surgery: A perspective.

Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

Evaluation of Telemedicine Experience: Artificial Intelligence in Cancer Care

Background. To date, one of the key tasks of our time, which has yet to be solved, is to provide medical services remotely using constantly advancing information and communication technologies. It is medical sciences that urgently needs ICT tools for various scopes: providing medical care in remote regions, organizing continuous education of medical personnel, managing electronic patient records, implementing mobile diagnostic devices, and managing network of branches or medical facilities at the institutional level. Telemedicine is a necessity, especially for patients with comorbid chronic diseases like cancer. Objective. To evaluate utilization of digital technologies at various stages of cancer care, through the example of artificial intelligence. Materials and methods. A systematic literature review was conducted using PRISMA. The search was carried out in such databases as Elibrary, PubMed, MEDLINE, Google Scholar by keywords “Telemedicine in oncology”, “COVID-19 in oncology”, “Artificial Intelligence”, “Digital Technologies”, “Data Personalization”, and “Teleoncology”. Publications not primarily focused on evaluating telemedicine technologies in oncology were excluded from the review process. Results. The literature review was conducted to consider the rapid integration of telemedicine since the pandemic, the risks associated with the widespread use of telemedicine in oncology, and the use of artificial intelligence in oncology, including the early detection and diagnosis of cancer. In conclusion, it is noted that the digitalization of healthcare radically changes the clinical workflow by expanding the possibilities for obtaining information from both medical staff and patients. Experience-based medicine is being replaced by an evidence-based, patient-centered approach. The rapidly developing artificial intelligence technologies will continue to have a great impact on the oncology in the near future. Both medical staff and scientific community must be prepared for the coming changes.

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

Exploring the literature on artificial intelligence use in oncology.

e13642 Background: Artificial Intelligence (AI) allows machines to analyze data in a way that mimics human intelligence. AI has been used in oncology diagnostics such as radiology and pathology. However, the specific processes and tasks that AI can support, especially in oncology, have not been fully explored. Methods: The electronic databases PubMed and MEDLINE were searched from September 1, 2018, to September 1, 2023, for publications related to the use of AI in oncology. Inclusion criteria consisted of English language, article type “study” and keywords Artificial Intelligence, Healthcare, Predictive/Prediction, Fever, Cytokine Release Syndrome, Nursing, Patient Care, Oncology, and Neutropenic Fever applied in a progressive manner to narrow the search. Results: The term Artificial Intelligence yielded over 124,385 studies. This number was reduced to 466 when adding “healthcare, predictive/prediction, and oncology.” Adding “patient care” further reduced the number to 102. Out of the 102, seven studies identified included the term “nursing” and focused on clinician decision support, readmissions, palliative care, and scheduling. One identified study discussed the potential benefits of AI-generated algorithms for risk stratification related to cytokine release syndrome. The keywords “neutropenic fever” did not generate any results. Six studies were identified by adding in the term “fever,” which focused on AI use in specialties outside of oncology. The literature reviewed shows that AI has had limited use in oncology outside of radiology and pathology. Only one study suggested the use of AI would be beneficial in predicting symptoms related to treatment. Small studies were identified showing the value of AI in increasing adherence to schedules, reducing readmissions, and providing precision medicine. Conclusions: AI is a promising tool for the future of oncology. The current availability of AI within oncology practices has been limited. Further studies should be undertaken to identify how AI can reduce provider burnout, care costs, and early identification of symptoms to prevent adverse events.

Artificial Intelligence in Oncology

Artificial Intelligence in Oncology

The digital revolution in pathology: Towards a smarter approach to research and treatment.

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care

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.

From image analysis to clinical decision support: applications of artificial intelligence in oncology

From image analysis to clinical decision support: applications of artificial intelligence in oncology

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.

AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Assessment of ChatGPT-3.5's Knowledge in Oncology: Comparative Study with ASCO-SEP Benchmarks.

ChatGPT (Open AI) is a state-of-the-art large language model that uses artificial intelligence (AI) to address questions across diverse topics. The American Society of Clinical Oncology Self-Evaluation Program (ASCO-SEP) created a comprehensive educational program to help physicians keep up to date with the many rapid advances in the field. The question bank consists of multiple choice questions addressing the many facets of cancer care, including diagnosis, treatment, and supportive care. As ChatGPT applications rapidly expand, it becomes vital to ascertain if the knowledge of ChatGPT-3.5 matches the established standards that oncologists are recommended to follow. This study aims to evaluate whether ChatGPT-3.5's knowledge aligns with the established benchmarks that oncologists are expected to adhere to. This will furnish us with a deeper understanding of the potential applications of this tool as a support for clinical decision-making. We conducted a systematic assessment of the performance of ChatGPT-3.5 on the ASCO-SEP, the leading educational and assessment tool for medical oncologists in training and practice. Over 1000 multiple choice questions covering the spectrum of cancer care were extracted. Questions were categorized by cancer type or discipline, with subcategorization as treatment, diagnosis, or other. Answers were scored as correct if ChatGPT-3.5 selected the answer as defined by ASCO-SEP. Overall, ChatGPT-3.5 achieved a score of 56.1% (583/1040) for the correct answers provided. The program demonstrated varying levels of accuracy across cancer types or disciplines. The highest accuracy was observed in questions related to developmental therapeutics (8/10; 80% correct), while the lowest accuracy was observed in questions related to gastrointestinal cancer (102/209; 48.8% correct). There was no significant difference in the program's performance across the predefined subcategories of diagnosis, treatment, and other (P=.16, which is greater than .05). This study evaluated ChatGPT-3.5's oncology knowledge using the ASCO-SEP, aiming to address uncertainties regarding AI tools like ChatGPT in clinical decision-making. Our findings suggest that while ChatGPT-3.5 offers a hopeful outlook for AI in oncology, its present performance in ASCO-SEP tests necessitates further refinement to reach the requisite competency levels. Future assessments could explore ChatGPT's clinical decision support capabilities with real-world clinical scenarios, its ease of integration into medical workflows, and its potential to foster interdisciplinary collaboration and patient engagement in health care settings.

Artificial Intelligence in Oncology: Present Potential, Prospective Prospects, And Ethical Reviews

Over the last ten years, Artificial Intelligence (AI) has significantly contributed to solving several health issues, such as cancer.Deep Learning (DL), a subset of adaptable AI that facilitates automated identification of important characteristics, is rapidly used in manyfundamental and clinical cancer investigation domains. This review provides a comprehensive overview of recent instances of AI utilizedin oncology. It highlights how DL techniques have effectively resolved previously deemed unsolvable issues and discusses the challengesthat must be addressed for the wider implementation of such applications. In addition, we emphasize valuable resources and datasets thatmight facilitate the use of AI in cancer research. In the next decade, the development of novel AI methods and their practical use willprovide valuable knowledge in the field of cancer. The advancement of AI technology has proven rapid in recent times and is beingincorporated into every facet of life. The medical profession is also advancing in the deployment of AI-equipped medical equipment. AI isanticipated to have a significant impact on achieving the present worldwide movement towards precision medicine. This article offers acomprehensive summary of the historical development of AI and the current advancements in medical AI, with a specific emphasis oncancer.In addition, while AI has significant promise, several unresolved concerns exist.

Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals’ perspectives on cancer care and artificial intelligence

BackgroundThe integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies.MethodsA mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO.ResultsThe survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care.Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations.ConclusionHCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.

Artificial Intelligence in Oncology: The Wins, The Challenges, and How We Can Deliver on Personalized Cancer Care

The development of effective, targeted therapies for cancer remains one of the primary goals in precision medicine, requiring the input of multiple disciplines across the diagnostic and therapeutic landscape. Despite the signifi­cant role of cancer care in medicine, there is a forecasted shortfall of physicians able to diagnose and treat patients along their cancer diagnosis journey, which is only projected to worsen in the coming decades. Artificial intelligence (AI), and its manifestations in both machine learning and deep learning, are poised to un­burden physicians from many laborious and repetitive tasks in diagnostic anal­yses, while also providing insight into prospective therapeutic options. AI will increasingly facilitate decision-making as well as improve the accuracy and rel­evance of therapeutic recommendations. This collaborative interaction between algorithms and physicians promises to enhance the field of precision medicine and further enable optimal patient care.

Intelligent Detection for Pancreatic Cancer Diagnosis: Future Directions

Introduction: Artificial intelligence is one of the most modern information systems that bases its operation on analogous functioning to the human mind. Particular hopes are placed in the treatment of diseases considered incurable. One such disease is pancreatic cancer, which has an extremely poor prognosis. Thanks to the use of machine learning and deep learning, artificial intelligence analyzes the available images taken during computed tomography and compares them with the introduced changes characteristic of pancreatic cancer. The advantage of the machines is the ability to analyze data, scans and images in a fraction of a second. Material and methods: The authors reviewed the peer-reviewed international literature describing the application of artificial intelligence in oncology. Databases such as PubMed, SCOPUS and Web of were searched with the keywords "artificial intelligence", "oncology" and "pancreatic cancer". The review included articles from 2004-2023. Discussion: Pancreatic cancer is an important topic of research due to unsatisfactory treatment results and the lack of screening tests allowing for quick detection of people at risk. Artificial intelligence is becoming a promising solution for asymptomatic patients, who can be classified into risk groups much faster than traditional methods. Unlike humans, intelligent machines can work faster and continuously, delivering content and results much faster. Conclusions: Artificial intelligence is a promising tool in the diagnosis of pancreatic cancer. Research results indicate that it is the most widely used in the field of oncological radiology, thanks to which imaging of early stages of cancer is becoming easier to detect. There is a need to verify machine methods and avoid bias when privatizing smart machines. The good of the patient should always come first, especially in a disease with an unfavorable prognosis, such as pancreatic cancer.