Artıfıcıal Intellıgence Methods in Oncology

Focus issue: Artificial intelligence in medical physics.

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.

Within the next decade, the need for automation, intelligent data handling and pre-processing is expected to increase in order to cope with the vast amount of information generated by a heavily connected and digitalised world. Over the past decades, modern computer networks, infrastructures and digital devices have grown in both complexity and interconnectivity. Cyber security personnel protecting these assets have been confronted with increasing attack surfaces and advancing attack patterns. In order to manage this, cyber defence methods began to rely on automation and (artificial) intelligence supporting the work of humans. However, machine learning (ML) and artificial intelligence (AI) supported methods have not only been integrated in network monitoring and endpoint security products but are almost omnipresent in any application involving constant monitoring, complex or large volumes of data. Intelligent IDS, automated cyber defence, network monitoring and surveillance as well as secure software development and orchestration are all examples of assets that are reliant on ML and automation. These applications are of considerable interest to malicious actors due to their importance to society. Furthermore, ML and AI methods are also used in audio-visual systems utilised by digital assistants, autonomous vehicles, face-recognition applications and many others. Successful attack vectors targeting the AI of audio-visual systems have already been reported. These attacks range from requiring little technical knowledge to complex attacks hijacking the underlying AI.With the increasing dependence of society on ML and AI, we must prepare for the next generation of cyber attacks being directed against these areas. Attacking a system through its learning and automation methods allows attackers to severely damage the system, while at the same time allowing them to operate covertly. The combination of being inherently hidden through the manipulation made, its devastating impact and the wide unawareness of AI and ML vulnerabilities make attack vectors against AI and ML highly favourable for malicious operators. Furthermore, AI systems tend to be difficult to analyse post-incident as well as to monitor during operations. Discriminating a compromised from an uncompromised AI in real-time is still considered difficult.In this paper, we report on the state of the art of attack patterns directed against AI and ML methods. We derive and discuss the attack surface of prominent learning mechanisms utilised in AI systems. We conclude with an analysis of the implications of AI and ML attacks for the next decade of cyber conflicts as well as mitigations strategies and their limitations.

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.

Universal artificial intelligence workflow for factory energy saving: Ten case studies

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Smart manufacturing technologies have gained significant attention in the industrial sector due to their potential to revolutionize traditional manufacturing processes. Among these technologies, artificial intelligence (AI) methods have emerged as powerful tools for enhancing efficiency, productivity, and decision-making in manufacturing operations. This paper presents an analysis of smart manufacturing technologies for industry using AI methods. The analysis focuses on the application of AI techniques such as machine learning, deep learning, and data analytics in various aspects of smart manufacturing, including predictive maintenance, process optimization, quality control, and supply chain management. The paper provides an overview of the key AI methods employed in smart manufacturing and discusses their benefits and challenges. It also highlights case studies and real-world implementations of AI-based smart manufacturing systems. The findings of this analysis demonstrate the significant contributions of AI methods in enabling intelligent and autonomous manufacturing systems. The paper concludes with insights into the future directions and potential impact of AI-driven smart manufacturing technologies in industry, emphasizing the importance of continued research and development in this field to unlock the full potential of smart manufacturing in the industry. Smart manufacturing technologies have revolutionized the industrial sector by enhancing productivity, efficiency, and flexibility. Artificial intelligence (AI) methods, such as machine learning and data analytics, play a crucial role in enabling smart manufacturing systems to optimize processes and make informed decisions. This research paper aims to analyse the application of AI methods in smart manufacturing technologies. The study explores various AI-based approaches used in different stages of smart manufacturing, including data acquisition, data analysis, process optimization, and predictive maintenance. The research provides insights into the benefits, challenges, and potential future developments of AI in smart manufacturing, offering valuable guidance for industries aiming to implement these technologies.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms.

Despite of the success of artificial intelligent (AI) methods in many domains, there is big dilemma for AI when applying to air traffic management (ATM). That is AI researchers have long stated their AI methods are effective and reliable enough to handle many ATM problems, while human controllers still refuse to adopt such AI methods. We believe the dilemma is not about whether AI methods is effective or reliable enough, but about why human controllers should be replaced by AI methods. In other words, as long as an AI method aims to compete and replace human controllers, it will be confronted with the difficulty of not being accepted by human controllers. To address this dilemma, this paper proposes a new thinking about applying AI methods, i.e., an AI method should be developed in such a way of assisting human controllers, but never in the way of competing and replacing human controllers. This new thinking is called human-machine coevolutionary intelligence (HMCEI). A methodological framework of HMCEI is further developed for decision-making support of ATM, in order to demonstrate the concept of HMCEI is practicably possible.

Assessing perspectives on artificial intelligence applications to gastroenterology

This paper presents an approach of intelligent fault classification of induction motor bearing (IMB) using several artificial intelligent (AI) methods. The most common AI methods are FeedForward Neural Network (FFNN), Elman Network (EN), Radial Basis Function Network (RBFN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The data of IMB fault is obtained from Case Western Reserve University website in form of vibration signal. For further analysis these datas are converted from time domain into frequency domain through Fast Fourier Transform (FFT) in order to acquire more fault signs during pre-processing stage. Then, during features extraction stage, a set of 16 features from vibration and pre-processing signal are extracted. Subsequently, a distance evaluation technique is used as features selection, in order to select only salient features. Lastly, during fault classification several AI methods are examined, where results are compared and the optimum AI method is selected.

RBF neural network and support vector machine (SVM), two Artificial Intelligent (AI) methods, have been extensively applied on machinery fault diagnosis. Aero-engine, as one kind of rotating machine with complex structure and high rotating speed, has complicated vibration faults. As one kind of AI methods, RBF neural network has the advantages of fast learning, high accuracy and strong self-adapting ability. Support vector machine, another AI method, only needs a small quantity of fault data samples to train the classifier and does not need to extract signal features. In this paper, the applications of two AI methods on aero-engine vibration fault diagnosis are introduced. Firstly, the principles and algorithm of both two methods are presented. Secondly the fundamentals of two-shaft aero-engine vibration fault diagnosis are described and gotten the standard fault samples (training samples) and simulation samples (testing samples). Third, two AI methods are applied to the vibration fault diagnosis and obtained the diagnostic results. Finally, the advantages and disadvantages of the two methods are compared such as the computing speed, accuracy of diagnosis and complexity of algorithm, and given a suggestion of selecting the diagnostic methods.

Invited Commentary: Prostate Cancer Diagnosis-Challenges and Opportunities for Artificial Intelligence.

No abstract.

Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user-related variability. The aim of this study was to assess within-patient test-retest reproducibility of LV GLS measured by the novel AI method in repeated echocardiograms recorded by different echocardiographers and to compare the results to manual measurements. Two test-retest data sets (n=40 and n=32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by 2 different echocardiographers at each center. For each data set, 4 readers measured GLS in both recordings using a semiautomatic method to construct test-retest interreader and intrareader scenarios. Agreement, mean absolute difference, and minimal detectable change (MDC) were compared to analyses by AI. In a subset of 10 patients, beat-to-beat variability in 3 cardiac cycles was assessed by 2 readers and AI. Test-retest variability was lower with AI compared with interreader scenarios (data set I: MDC= 3.7 vs 5.5, mean absolute difference= 1.4 vs 2.1, respectively; data set II: MDC= 3.9 vs 5.2, mean absolute difference= 1.6 vs 1.9, respectively; all P<.05). There was bias in GLS measurements in 13 of 24 test-retest interreader scenarios (largest bias, 3.2 strain units). In contrast, there was no bias in measurements by AI. Beat-to-beat MDCs were 1,5, 2.1, and 2.3 for AI and the 2 readers, respectively. Processing time for analyses of GLS by the AI method was 7.9±2.8seconds. A fast AI method for automated measurements of LV GLS reduced test-retest variability and removed bias between readers in both test-retest data sets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.