Artificial intelligence in gynecologic oncology: A cautionary look in the Indian context.

DOI: http://dx.doi.org/10.5915/23-1-15227 Gynecologic oncology has a unique status in the field of oncology. Due to easy accessibility, early detection by cytology and first successful radiotherapy were achieved in cancer of the cervix. In addition, the first successful radical surgery and the first successful chemotherapy were in the field of gynecologic oncology. Gynecologic oncology is always in evolution and our aim in this article is to review the new approaches to diagnosis, staging, and treatment.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

Fluorescence-guided surgery (FgS) is increasingly used across oncologic specialties to enhance intraoperative visualisation of tumour tissue and lymphatic drainage; however, its clinical impact remains limited by heterogeneous tracer uptake, variable signal intensity, and reliance on subjective visual interpretation, leading to inter-operator variability, uncertainty at tumour margins, residual disease, and inconsistent nodal assessment. This narrative review examines the role of artificial intelligence (AI) in addressing these limitations, synthesising evidence published between January 2000 and December 2025 across neuro-oncology, gynaecological oncology, and thoracic oncology. In neuro-oncology, early clinical and preclinical studies have directly evaluated real-time AI-enhanced interpretation of intraoperative fluorescence, including quantitative analysis of 5-aminolevulinic acid (5-ALA) and hyperspectral imaging, providing proof-of-concept evidence that AI can augment margin detection beyond subjective visual assessment. In contrast, gynaecological and thoracic oncology currently lack validated studies in which AI directly interprets intraoperative fluorescence signals, despite fluorescence imaging being clinically established in both fields; instead, AI development in these specialties has progressed primarily in adjacent domains such as radiomics, digital pathology, risk stratification, surgical planning, and intraoperative computer vision, demonstrating technical maturity but limited integration into fluorescence-guided decision-making. Overall, the available evidence supports proof-of-concept feasibility for real-time AI-enhanced fluorescence interpretation in neuro-oncology, while identifying a clear translational gap in gynaecological and thoracic oncology that warrants targeted research to integrate existing AI capabilities into intraoperative fluorescence-guided surgery.

Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. The aim of this study was to review the role of AI in gynecologic oncology. Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

Artificial intelligence has contributed significantly to solving various medical problems, including cancer, over the past decade. Artificial intelligence is being applied more and more in various fields of cancer research. Today, artificial intelligence methods provide physicians with important privileges in making decisions, providing more effective service to managers and minimizing costs, reducing the workload of healthcare professionals, and receiving the treatment with the highest accuracy rate for the patient and the least error rate. This review can be evaluated in two categories. First of all, common diseases in the field of oncology and the use of artificial intelligence in the field of oncology are supported with examples. In the second category, 23 recent studies in the field of oncology in the literature summary table; artificial intelligence methods are divided into categories and accuracy rates are presented. It is thought that the rapidly developing artificial intelligence technology will continue to have a great impact in the field of cancer in the near future. As a result, it is thought that physicians and researchers should include artificial intelligence training courses in their multidisciplinary study and training curricula, keeping pace with the digitalizing new age in healthcare, with the widespread use of artificial intelligence-based clinical decision support systems, personalized medicine, time in diagnosis and treatment, reducing the error rate and it will provide an important advantage in terms of patient and employee satisfaction and both cost-effective and quality service delivery.

Simple SummaryArtificial intelligence (AI) technology has been advancing rapidly in recent years and is being implemented in society. The medical field is no exception, and the clinical implementation of AI-equipped medical devices is steadily progressing. In particular, AI is expected to play an important role in realizing the current global trend of precision medicine. In this review, we introduce the history of AI as well as the state of the art of medical AI, focusing on the field of oncology. We also describe the current status of the use of AI for drug discovery in the oncology field. Furthermore, while AI has great potential, there are still many issues that need to be resolved; therefore, we would provide details on current medical AI problems and potential solutions.In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Artificial intelligence (AI) is a sort of new technical science which can simulate, extend and expand human intelligence by developing theories, methods and application systems. In the last five years, the application of AI in medical research has become a hot topic in modern science and technology. Gynecological malignant tumors involves a wide range of knowledge, and AI can play an important part in these aspects, such as medical image recognition, auxiliary diagnosis, drug research and development, treatment scheme formulation and other fields. The purpose of this paper is to describe the progress of AI in gynecological malignant tumors and discuss some problems in its application. It is believed that AI improves the efficiency of diagnosis, reduces the burden of clinicians, and improves the effect of treatment and prognosis. AI will play an irreplaceable role in the field of gynecological malignant oncology and will promote the development of medicine and further promote the transformation from traditional medicine to precision medicine and preventive medicine. However, there are also some problems in the application of AI in gynecologic malignant tumors. For example, AI, inseparable from human participation, still needs to be more “humanized”, and needs to further protect patients’ privacy and health, improve legal and insurance protection, and further improve according to local ethnic conditions and national conditions. However, it is believed that with the continuous development of AI, especially ensemble classifier, and deep learning will have a profound influence on the future of medical technology, which is a powerful driving force for future medical innovation and reform.

Simple SummaryIn an age where technology is deeply intertwined with healthcare, this review focuses on the synergistic role of artificial intelligence (AI) and radiomics in the management of urological cancers, particularly bladder, kidney, and prostate cancers. Our comprehensive review explores how AI’s rapid data-processing capabilities, combined with the intricate image analysis offered by radiomics, are reshaping cancer diagnosis and treatment. We delve into current research findings to illustrate how these innovative technologies are steering oncology toward more accurate, personalized care. This summary is crafted to be accessible, avoiding complex medical jargon and extensive academic references, aiming to highlight the essence and potential impact of these advancements. Our objective is to showcase how AI and radiomics are instrumental in early cancer detection, informed therapeutic decisions, and potentially improved patient outcomes. The research compiled in this paper not only charts a course for the future integration of these technologies in cancer care but also underscores the emerging trend towards patient-centric strategies in the medical community, offering renewed hope and direction in the fight against these cancers.This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the “black box” nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.

Purpose This study aims to investigate the role of the regulatory environment in influencing how the perceived transparency and competence of artificial intelligence (AI) systems affect trust in those systems and conflict resolution ability in India’s financial institutions. It examines the role of trust as an intermediary variable between these AI system attributes and organisational outcomes by employing theories that include trust theory, signalling theory, social exchange theory, the technology acceptance model and institutional theory. Design/methodology/approach Utilizing survey data from 885 employees working in both public and private sector banks across India, collected through a multi-stage sampling method, this study employed structural equation modelling (SmartPLS) to evaluate the conceptual model. The analysis focused on examining both the direct and indirect effects, as well as the mediation of relationships among AI attributes, trust, the regulatory environment and conflict resolution outcomes. Findings The results indicate that perceived transparency and competence are significant predictors of trust in AI systems, and that trust in AI systems is a positive predictor of conflict resolution effectiveness. In addition, trust is an important mediator between AI attributes and operational outcomes. Finally, the regulatory environment positively moderates the relationship between transparency and trust, highlighting the importance of institutional legitimacy and safeguards in fostering employee confidence in AI systems. Research limitations/implications These results have implications for developing AI systems that emphasise transparency, competence and trustworthiness. Regulatory bodies, including the RBI, must develop stronger governance frameworks, oversight mechanisms and AI-literacy initiatives to foster greater trust in AI systems and encourage their institutional adoption. Finally, this study presents practical advice for developing responsible and trustworthy AI systems in a highly regulated financial ecosystem. Originality/value To the best of the authors’ knowledge, this study is one of the first empirical studies in the Indian banking context to demonstrate the causal links between AI system attributes, trust mechanisms, regulatory variables and conflict resolution outcomes. By combining micro-level trust dynamics with macro-level institutional influences, this study provides a new, multi dimensional perspective on the use of AI in regulated financial service environments and expands our theoretical knowledge regarding the use of trust-based collaborative efforts between humans and AI systems.

Background: The field of medicine, both clinical and surgical, has recently been overwhelmed by artificial intelligence technology, which promises countless application scenarios and, above all, implementation in clinical practice and research. Novelties are riding the wave fast, but where do we stand? A small overview in gynecological oncology of future challenges, evidence already investigated, and possible scenarios to be derived was conducted. Methods: Both diagnostic and surgical work in the field of gynecological oncology was conducted, selecting the most interesting articles on the subject. Results: From the narrative review of the literature, it emerged how much further ahead the diagnostic field is at present compared to the surgical one, which appeared to be more limited to ovarian surgery. Most current evidence focuses on the role of different biomarkers in predicting diagnostic, prognostic, and treatment-integrated patterns. Conclusions: Everything we know to date is related to a dynamic photograph that is constantly and rapidly changing as much as AI is becoming inextricably linked to our medical field.

Simple SummaryIn the era of personalized medicine, Artificial Intelligence (AI) has emerged as a powerful tool with growing applications in the field of gynaecologic oncology. However, AI applications are encountered with several challenges derived from their “black-box” nature, which limits their adoption by clinicians. Surgical decision-making at cytoreductive surgery for epithelial ovarian cancer (EOC) is a complex matter, and an accurate prediction of surgical effort is required to ensure the good health and care of patients. We combined high-performance AI modeling with an eXplainable Artificial Intelligence (XAI) framework to explain feature effects and interactions associated with specific threshold surgical effort using data from a single public institution. We revealed features not routinely measured in the clinical practice, including human factors that could be responsible for the variation in the surgical effort. Selective decreased surgical effort may be associated with the surgeon’s age. The use of XAI frameworks can provide actionable information for surgeons to improve patient outcomes in gynaecologic oncology.(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598–0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69–0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655–0.823; p < 0.95). We identified “turning points” that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.

623 Background: Artificial intelligence (AI) has demonstrated significant potential in oncology, improving diagnostics, treatment planning, and personalized medicine. However, its adoption in low- and middle-income countries (LMICs) like Cameroon remains underexplored. This study assesses the perceptions, barriers, and readiness of medical, surgical, and radiation oncologists in Cameroon toward AI integration in oncology practice. Methods: We conducted a cross-sectional survey among oncologists practicing in Cameroon, distributing a structured questionnaire both electronically and in person. The survey assessed AI familiarity, perceived benefits, concerns, barriers, and willingness to adopt AI-based technologies. We used Python and SPSS Version 30 to analyze the data using descriptive statistics, chi-square tests, and logistic regression. Results: A total of 29 oncologists participated, with the majority aged 31–40 years (82.4%) and predominantly specializing in medical oncology (55.2%). AI familiarity was moderate (36%), with 44% reporting limited knowledge. While 80-90% of respondents recognized AI's potential to improve diagnostic accuracy and treatment planning, concerns included ethical/privacy issues (50-60%), reduced doctor-patient interaction (40-50%), and risks of misdiagnosis (15%). Despite these concerns, 82.1% expressed moderate-to-high willingness to adopt AI, citing the need for structured AI training (89.7%) and regulatory guidelines. Barriers included cost (69%), lack of training (65.5%), and infrastructure constraints (62.1%). The majority (96.2%) were willing to participate in AI training programs. Conclusions: While oncologists in Cameroon acknowledge AI’s potential benefits in the field of oncology, significant barriers related to training, ethics, and infrastructure hinder adoption. Tackling these issues with AI education, clear policies, and better digital healthcare systems is essential for making the most of AI, which can improve patient care by making diagnoses more accurate, personalizing treatment, streamlining clinical processes, and supporting data-based decisions in low- and middle-income countries.

The technical breakthrough of artificial intelligence (AI) in the field of oncology has moved from the laboratory to the clinic, but the realization of its social value is still facing the "last mile" dilemma. According to the WHO, there are more than 19 million new cancer cases worldwide every year, but the algorithmic advantages of AI are in sharp contrast to the uneven distribution of resources: while high-income countries are using AI to optimize personalized treatment programs, low-income regions are difficult to enjoy the technical dividends due to the lack of data. This work takes the " Technology-Ethics-Fairness" framework as the starting point to explore how to build a more inclusive AI oncology research ecology through interdisciplinary cooperation. Despite the outstanding performance of AI in the fields of tumor image recognition and genomics analysis, most studies focus on technical performance optimization and ignore the impact of social and cultural differences on the implementation of algorithms. For example, the driver gene mutation characteristics of lung cancer in Asian populations are significantly different from those in Europe and the United States, but the proportion of non-European ancestry samples in the public database is less than 10%, which leads to bias when the model is applied across regions. Furthermore, the inherent "black box" nature of AI decision-making exacerbates the crisis of trust between doctors and patients, especially in areas with limited medical resources, where technical authority may override clinical experience. To foster responsible and equitable AI in oncology, we propose three key pillars so that AI research can better serve society: (1) Data Equity: Establishing a global federated learning consortium for privacy-preserving, multi-omic data sharing to enable cross-regional model training. (2) Interpretability &amp; Trust: Developing "decision traceability" tools that dynamically link AI outputs to clinical guidelines and supporting evidence. (3) Proactive Ethics: Integrating ethical impact assessments, informed by frameworks like the EU AI Act, into clinical trial design, including explicit metrics for equity and bias. The ultimate value of AI should not stop at improving the efficiency of diagnosis and treatment but also reshape the global collaboration network of cancer research. It is recommended to establish an international certification standard of "AI for Oncology," covering the dimensions of data representativeness, algorithm transparency, and cross-cultural adaptability. At the same time, bridging the technology gap through immersive medical education can help doctors in underdeveloped countries or regions to practice AI-assisted decision-making on 3D tumor models. As AI evolves from "technology enabler" to "ecological builder," cancer research will break through the boundaries of regions and disciplines and realize exponential growth of social value. We look forward to seeing more solutions that integrate technological innovation and humanistic care in the future. Citation Format: Zhicheng Du, Lijin Lian, Wenji Xi, Yu Zheng, Gang Yu, Hui-Yan Luo, Peiwu Qin. Artificial intelligence enables the ethical reconstruction and social value realization of global cancer research: From technological innovation to humanistic care [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B024.

1564 Background: Artificial intelligence (AI) is increasingly being incorporated into the oncology field as a tool to support clinical decisions. AI tools such as ChatgGPT or OpenEvidence provide responses to user-generated queries, whereas some institutions or companies such as Primum, Inc, offer consultations with actual experts who provide personalized responses to clinician-submitted real-world cases. However, the value of AI tools to augment expert consultations continues to evolve. We report results of a study comparing AI versus expert oncologists' responses to 107 real-world hematology/oncology cases. Methods: Among 107 cases, inquiries included lymphomas (30), myeloma (24), leukemias (11), myeloid disorders (10), as well as classical hematology (32), assessed among 20 experts. Responses to de-identified cases submitted by practicing clinicians to Primum (www.primum.co) between June 2022-July 2023 were compared to GPT-4 responses (openai.com/chatgpt). The instructional prompt to GPT-4 was, "You are an expert oncologist conversing with another oncologist as a peer. You prefer to rely on guidelines and data published in reputable medical journals when responding.” Five expert faculty at our institution adjudicated the blinded comparative responses, including their preference, quality and practical value scores, and prediction of which response was AI generated. Comparison of scores was by t-test to generate P-values between expert and AI groups, and Pearson correlation was used for comparisons between adjudication scores. Results: Expert responses were preferred by &gt; 50% of adjudicators in 75% of cases (deviation ±25%). Randomized AI responses were correctly identified 90% of the time. Mean expert vs AI scores (Likert scale 0-4) for quality (2.0 vs 2.1, P = 0.9) and practical value (2.1 vs 2.1, P = 0.9) were equivalent. Interestingly, AI responses were preferred in 46% (n = 15) of classical hematology and 31% (n = 9) of lymphoma cases, largely due to being more concise. However there was no concordance between high practical value scores and disease subtype for either group. Conclusions: : Expert physician responses were preferred over AI responses for most of the cases based on the level of detail presented, suggesting an implicit value of personalized responses compared to AI. Results showed no significant differences in quality or practical utility between AI generated responses and those from experts, reflecting a similarity in the information extracted from standardized guidelines, and potentially adding value of AI in supporting clinical decision making. Our findings are limited by the broad coverage of hematologic conditions for which experts and guidelines vary. Overall, these data suggest that while AI can supplement knowledge of management paradigms by providing basic management strategies, at present it cannot replace personalized expert consultation in clinical practice.

e13587 Background: The use of artificial intelligence (AI) and machine learning is becoming more common and is expected to expand further in order to meet the needs of our ever-evolving healthcare system. In oncology, AI and machine learning are already being explored in various applications. Despite AI’s importance, there is sparse formal teaching on AI incorporated into medical schools’ curricula and residency training programs. In this study, we examined the perceptions and knowledge of Canadian oncology residents and fellows with respect to AI technologies. Methods: An electronic, anonymous, questionnaire-based survey was distributed to residents and fellows in medical and radiation oncology programs across Canada. Survey questions spanned areas of demographics, familiarity with AI, personal attitudes towards AI, and perspectives regarding AI use in different specialties. Approval was obtained from the Queen’s Research Ethics Board prior to conducting this study. Mixed-methods statistical analysis is ongoing. Qualitative data will be analyzed using thematic analysis. Univariable and multivariable regressions will be conducted to identify any correlation between perception or knowledge of AI and demographic factors. Results: Fifty-seven participants responded in total. Most residents (67%) agreed or strongly agreed that it was important they learn about AI. Seventy percent indicated that, if given the chance, they would like to learn more about AI, yet the majority of participants (88%) indicated they had not received formalized teaching. Disciplines that were felt to be most associated with AI were radiology (98%), radiation oncology (84%), and pathology (58%). With respect to the field of radiation oncology, 98% of respondents felt that AI had the potential to replace some, most, or all medical activities. A perceived barrier to understanding AI was a lack of knowledge of mathematics and programming (63%). Respondents indicated that their preferred formats for learning about AI would be workshops (78%), lectures (60%), and collaborative activities with other departments (46%). Conclusions: Our results show that Canadian oncology residents’ sense that AI is important and relevant to their area of training. Despite this, they have not received education on these topics. Thus, formalized teaching, such as lectures and workshops, would be perceived as beneficial by most Canadian oncology residents.