mirror of https://github.com/kortix-ai/suna.git
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"output": "MCP Tool Result from EXA:\n\n{\n \"requestId\": \"51aa96dce871e90d752715996f940be5\",\n \"autopromptString\": \"AI artificial intelligence cancer research papers\",\n \"resolvedSearchType\": \"neural\",\n \"results\": [\n {\n \"id\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC11113055/\",\n \"title\": \"The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide\",\n \"url\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC11113055/\",\n \"publishedDate\": \"2024-05-22T00:00:00.000Z\",\n \"author\": \"\",\n \"score\": 0.4339243769645691,\n \"text\": \"\\n \\n \\n \\n Abstract \\n Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care. \\n Keywords: cancer, artificial intelligence, machine learning, soft computing, machine vision, deep learning Artificial Intelligence Application to Cancer Research \\n Cancer continues to be a significant global health challenge, with early diagnosis, accurate prognosis, and personalized treatment being critical for improving patient outcomes. In recent years, the application of artificial intelligence (AI) has emerged as a promising approach to revolutionize cancer care, offering unprecedented opportunities for advancements in cancer research and clinical practice. Figure 1 shows the number of papers published on the application of AI to cancer research and the number of AI-based models applied to different cancers, highlighting the rapidly advancing field of AI in cancer research. \\n Figure 1. \\n \\n (A) The number of papers published in the field of “ AI applications to cancer research” between 2010 and 2023 (keywords searched: “AI” and “Cancer”). (B) The number of AI-based models applied to different types of cancers between 2010 and 2022 (keywords searched: “AI” and “Cancer Type”). For A and B, the information was extracted from the PubMed database. \\n 1 \\n AI has shown promising results in studying different types of human cancers, 2 – 5 including but not limited to cervical cancer, 6, 7 pancreatic cancer, 3, 8 breast cancer, 9, 10 colorectal cancer, \\n 11 \\n ovarian cancer, \\n 12 \\n laryngeal cancer, \\n 13 \\n brain cancer, 14, 15 and lung cancer. 4, 16, 17 In a scoping review, the extent of the use of AI and ML protocols for cancer diagnosis in prospective settings was explored. \\n 2 \\n A literature review was also conducted on AI-driven digital cytology-based cervical cancer screening and highlighted the potential of this technology in resource-constrained settings. \\n 6 \\n Another scoping review was conducted on the use of AI for the prediction and early diagnosis of pancreatic cancer, emphasizing the importance of early dete\",\n \"favicon\": \"https://pmc.ncbi.nlm.nih.gov/static/img/favicons/favicon-32x32.png\"\n },\n {\n \"id\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC10697339/\",\n \"title\": \"Artificial Intelligence Applications for Biomedical Cancer Research: A Review\",\n \"url\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC10697339/\",\n \"publishedDate\": \"2023-11-05T00:00:00.000Z\",\n \"author\": \"\",\n \"score\": 0.42629361152648926,\n \"text\": \"\\n \\n \\n \\n Abstract \\n Artificial intelligence (AI) has rapidly evolved and demonstrated its potential in transforming biomedical cancer research, offering innovative solutions for cancer diagnosis, treatment, and overall patient care. Over the past two decades, AI has played a pivotal role in revolutionizing various facets of cancer clinical research. In this comprehensive review, we delve into the diverse applications of AI across the cancer care continuum, encompassing radiodiagnosis, radiotherapy, chemotherapy, immunotherapy, targeted therapy, surgery, and nanotechnology. AI has revolutionized cancer diagnosis, enabling early detection and precise characterization through advanced image analysis techniques. In radiodiagnosis, AI-driven algorithms enhance the accuracy of medical imaging, making it an invaluable tool for clinicians in the detection and assessment of cancer. AI has also revolutionized radiotherapy, facilitating precise tumor boundary delineation, optimizing treatment planning, and enabling real-time adjustments to improve therapeutic outcomes while minimizing collateral damage to healthy tissues. In chemotherapy, AI models have emerged as powerful tools for predicting patient responses to different treatment regimens, allowing for more personalized and effective strategies. In immunotherapy, AI analyzes genetic and imaging data to select ideal candidates for treatment and predict responses. Targeted therapy has seen great advancements with AI, aiding in the identification of specific molecular targets for tailored treatments. AI plays a vital role in surgery by offering real-time navigation and support, enhancing surgical precision. Moreover, the synergy between AI and nanotechnology promises the development of personalized nanomedicines, offering more efficient and targeted cancer treatments. While challenges related to data quality, interpretability, and ethical considerations persist, the future of AI in cancer research holds tremendous promise for improving patient outcomes through advanced and individualized care. \\n Keywords: radiodiagnosis, nanotechnology, personalized treatment, diagnostics, precision medicine, cancer research, artificial intelligence Introduction and background \\n Artificial intelligence (AI) has been widely used in medical specialties [ 1]. The pioneering work had an impact on a wide range of clinical indications, including ophthalmology, radiology, dermatology, and others. The application of AI technologies to basic biology, pharmacology, and medicine has resulted in numerous performance breakthroughs, with some areas achieving performance comparable to human experts [ 2]. Early AI approaches were dominated by traditional symbol-based and information-based expert systems. Following that, the advent of machine learning (ML) resulted in revolutionary advances in AI. As a traditional AI technology, ML provides a plethora of algorithms that can improve determination or prediction accuracy with large amounts of \",\n \"favicon\": \"https://pmc.ncbi.nlm.nih.gov/static/img/favicons/favicon-32x32.png\"\n },\n {\n \"id\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC11131133/\",\n \"title\": \"Artificial Intelligence (AI) in Oncology: Current Landscape, Challenges, and Future Directions\",\n \"url\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC11131133/\",\n \"publishedDate\": \"2024-05-01T00:00:00.000Z\",\n \"author\": \"\",\n \"score\": 0.4145283102989197,\n \"text\": \"\\n \\n \\n \\n \\n. Author manuscript; available in PMC: 2024 Nov 1. Abstract \\n Artificial Intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. Introduction \\n Artificial Intelligence (AI) is increasingly being applied to all aspects of oncology. These applications have been set in motion by two fundamental shifts. The first is the development of new computational models and tools. In particular, advances in deep learning 1 over the past decade have made it feasible to learn complex patterns directly from real-world data, making it the core driver of AI advances in and out of healthcare 2. These advances in deep learning have been accompanied with rapid advances in graphical processing units (GPUs) and cloud computing that enable the development of increasingly large models trained on massive data sets. The second shift is the advancing digital landscape of oncology itself. This includes the storage of patient data in electronic medical record (EMR) systems, the digitization of radiology and pathology imaging 3, and the increasing adoption of routine genomic profiling 4. While this digitization is non-uniform across data modalities and clinical sites, there is increasing availability of detailed, longitudinal information for cancer patients. This data can be used to build and train AI models, and critically, the real-time availability of this data can enable individualized, clinically relevant AI predictions to further the goal of precision oncology 5. \\n Within this review, we aim to summarize the current landscape of AI in oncology. While the field can encompass numerous applications, including biological and drug discovery 6, this review specifically focuses on AI use cases targeting direct integration into clinical practice. This focus is motivated by the accelerated advancement of AI applications from development to clinical use. Additionally, we focus on AI approaches based on modern, deep learning methods, rather than other machine learning or rule-based methods. Finally, given the numerous possible applications and breadth of AI work, the review focuses on the four most common cancer types: Breast, Prostate, Lung, and Colorectal, accounting for 50% of all new cancer cases in 2023 7. AI applications for these cancer types serve as a good representation of the current state of the field, especially as their prevalence facilitates large-scale data collection and encourages clinical application. \\n To first motivate\",\n \"favicon\": \"https://pmc.ncbi.nlm.nih.gov/static/img/favicons/favicon-32x32.png\"\n },\n {\n \"id\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC11843581/\",\n \"title\": \"Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook\",\n \"url\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC11843581/\",\n \"publishedDate\": \"2025-02-04T00:00:00.000Z\",\n \"author\": \"\",\n \"score\": 0.4119340777397156,\n \"text\": \"\\n \\n \\n \\n Abstract \\n Background \\n Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL). Objective \\n This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field. Methods \\n A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included “Artificial Intelligence (AI),” “Machine Learning (ML),” and “Deep Learning (DL)” combined with “chemotherapy development,” “cancer diagnosis,” and “cancer treatment.” Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies. Conclusion \\n This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI’s potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI’s integration into oncology care. Keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), chemotherapy development, cancer diagnosis, cancer treatment Introduction \\n Artificial intelligence (AI) has been extensively applied across multiple medical fields, marking a transformative impact on diverse therapeutic areas, including ophthalmology, radiology, and dermatology. The integration of AI technologies into fundamental biology, pharmacology, and clinical medicine has triggered significant enhancements in performance, achieving benchmarks on that match or exceed human experts’ performance in specific domains ( 1, 2). \\n The potential of AI to revolutionize cancer research, diagnosis, and treatment is particularly notable given its advanced analytical capacities. The proliferation of large-scale cancer research datasets presents an unprecedented opportunity to amalgamate intricate research insights with vast data arrays, necessitating robust computational power to manage and interpret these complex information streams ( 1, 2). \\n Cancer, one of the most severe illnesses, remains the second leading cause of mortality worldwide, with its prevalence continuing to rise despite global efforts to combat it ( 1, 2). Current tools and procedures for early-stage cancer detection and diagnosis often lack ac\",\n \"favicon\": \"https://pmc.ncbi.nlm.nih.gov/static/img/favicons/favicon-32x32.png\"\n },\n {\n \"id\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC10312208/\",\n \"title\": \"Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach\",\n \"url\": \"https://pmc.ncbi.nlm.nih.gov/articles/PMC10312208/\",\n \"publishedDate\": \"2023-06-26T00:00:00.000Z\",\n \"author\": \"\",\n \"score\": 0.407356858253479,\n \"text\": \"\\n \\n \\n \\n Abstract \\n Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects. \\n Keywords: machine learning, artificial intelligence, treatment selection, cancer diagnosis, cancer-related mortality Introduction \\n Cancer is a significant public health issue globally, marked by an elevated incidence and mortality rate. 1 According to the GLOBOCAN 2020 database, approximately 19.3 million new cases and 10 million deaths have been reported annually. 2 Lung cancer remains the most common cause of cancer-related mortality, with expected 1.8 million fatalities, followed by stomach, liver, colorectal, and breast cancer. 2 The prevention and treatment of cancer remain difficult. 3 After heart disease, cancer remains the second leading cause of death in the United States. In 2023, there are projected to be 1.9 million new cancer cases (equivalent to around 5370 cases per day) and 609,820 deaths from cancer (equivalent to around 1670 deaths per day) in the US. The International Agency for Research on Cancer (IARC) has released a poster on known causes and prevention by organ site of human cancer. A description of known causes and prevention by organ site is provided in Figure S1. \\n The “Global Cancer Observatory” reports indicate that on a global scale, 37 individuals are diagnosed with cancer and over 19 individuals succumb to the disease every minute. Figure 1A shows the number of new cases in 2020 and Figure 1B shows the number of deaths in 2020 due to all cancers.\\n \\n Figure 1. \\n \\n The number of new cancer cases reported in 2020 and the number of deaths caused by these cancers. ( A). Estimated number of new cases worldwide in 2020 among both sexes. ( B) Estimated number of deaths worldwide in 2020 among both sexes. Reprinted from World Health Organization. © International Agenc\",\n \"favicon\": \"https://pmc.ncbi.nlm.nih.gov/static/img/favicons/favicon-32x32.png\"\n }\n ],\n \"costDollars\": {\n \"total\": 0.01,\n \"search\": {\n \"neural\": 0.005\n },\n \"contents\": {\n \"text\": 0.005\n }\n }\n}\n\n---\nTool Metadata: {\n \"server\": \"exa\",\n \"tool\": \"web_search_exa\",\n \"full_tool_name\": \"mcp_exa_web_search_exa\",\n \"arguments_used\": {\n \"query\": \"AI artificial intelligence cancer research papers\",\n \"num_results\": 10,\n \"type\": \"neural\",\n \"include_domains\": [\n \"arxiv.org\",\n \"pubmed.ncbi.nlm.nih.gov\",\n \"nature.com\",\n \"science.org\",\n \"ieee.org\"\n ]\n },\n \"is_mcp_tool\": true\n}",
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