Introduction to Machine Learning, Neural Networks, and Deep Learning.

Artificial intelligence in interdisciplinary life science and drug discovery research.

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

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.

Focus issue: Artificial intelligence in medical physics.

Plants meet machines: Prospects in machine learning for plant biology

Leukemia is a type of blood cancer that begins in the bone marrow and results in high numbers of abnormal white blood cells. Automated detection and classification of leukemia and its subtypes using artificial intelligence (AI) and machine learning (ML) algorithms plays a significant role in the early diagnosis and treatment of this fatal disease. This study aimed to review and synthesize research findings on AI-based approaches in leukemia detection and classification from peripheral blood smear images. A systematic literature search was conducted across four e-databases (Web of Science, PubMed, Scopus, and IEEE Xplore) from January 2015 to March 2023 by searching the keywords "Leukemia," "Machine Learning," and "Blood Smear Image," as well as their synonyms. All original journal articles and conference papers that used ML algorithms in detecting and classifying leukemia were included. The study quality was assessed using the Qiao Quality Assessment tool. From 1325 articles identified through a systematic search, 190 studies were eligible for this review. The mean validation accuracy (ACC) of the ML methods applied in the reviewed studies was 95.38%. Among different ML methods, modern techniques were mostly considered to detect and classify leukemia (60.53% of studies). Supervised learning was the dominant ML paradigm (79% of studies). Studies utilized common ML methodologies for leukemia detection and classification, including preprocessing, feature extraction, feature selection, and classification. Deep learning (DL) techniques, especially convolutional neural networks, were the most widely used modern algorithms in the mentioned methodologies. Most studies relied on internal validation (87%). Moreover, K-fold cross-validation and train/test split were the commonly employed validation strategies. AI-based algorithms are widely used in detecting and classifying leukemia with remarkable performance. Future studies should prioritize rigorous external validation to evaluate generalizability.

Machine Learning Applications in Orthopaedic Imaging.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Sensors support machine learning

Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit

<h3>Background</h3> New technologies, such as artificial intelligence (AI), supported by novel ways of linking and analysing data, are transforming the way that healthcare data is analysed. AI is increasingly being used to support healthcare delivery, and examples of palliative care application are emerging. AI is an umbrella term covering a variety of intertwined sub-concepts: machine learning refers to machine algorithms automatically improving themselves through experience, and neural networking refers to a form of this mimicking the way the human brain works. Deep learning is another form of machine learning, and natural language processing may refer to a variety of AI algorithms used to understand text intended for human recipients. However, the current scope of (and potential) use of AI in palliative care delivery has not been fully explored. The aim of this project was to define the scope of use of AI methodologies in palliative care studies. <h3>Methods</h3> A systematic review of literature was conducted in accordance with the PRISMA guidelines. Four electronic databases were searched, in addition to grey literature searches. AI was used as an umbrella term to include keyword searches for the following: machine learning, deep learning, neutral networks and natural language processing. <h3>Results</h3> Twenty-seven relevant articles were selected. The majority of studies described people with cancer (n=10, 37%), from general palliative (n=8, 30%) and intensive care populations (n=4, 15%). Studies using natural language processing were most common (n=12, 44%), with others mainly utilising machine learning (n=10, 37%), deep learning (n=3, 11%) and neural network (n=2, 8%) methodologies. A variety of outcomes were covered, with most studies predicting survival (n=8, 30%), identifying goals of care (n=6, 22%), analysing serious illness conversations (n=2, 9%) and reporting if palliative care best practice recommendations had been followed in clinical care (n=2, 9%). <h3>Conclusion</h3> Most palliative care AI studies report cancer, use natural language processing and machine learning methods, to predict survival and analyse goals of care. Future studies need to explore how different AI methods can support palliative care, whilst carefully assessing the risks and limitations, to ensure effective use in the management of serious illness.

Unleashing the Power of Machine Learning to Predict Myocardial Recovery After Left Ventricular Assist Device: A Call for the Inclusion of Unstructured Data Sources in Heart Failure Registries.

The review presents a comprehensive analysis of the latest advances in machine learning (ML), artificial neural networks (ANN), and deep learning (DL) in urologic oncology. As part of the study, the Russian and foreign scientific literature was ranked based on PubMed, MEDLINE, E-library, CYBERLENINKA, etc. The data related to the use of ML, ANN, and DL in the diagnosis and treatment of prostate cancer (PCa), bladder cancer (BC), testicular cancer, and kidney cancer was collected. Most often, ANN and ML in PCa were used for early diagnosis, prognosis, and personalized systemic treatment strategy development. ANN and DL models were trained with clinical parameters, NGS-sequencing results, Gleason scores, and digitized radiological, and histological images. Radiomics was also used to diagnose PCa, followed by analysis of special image texture features on a digital slide. In metastatic castration-resistant PCa, artificial intelligence (AI) algorithms were used to predict the response to docetaxel treatment. The prospects of using AI for tumor imaging during radical prostatectomy and when performing robot-assisted kidney resection were also addressed. A diagnostic approach for testicular malignancies based on computed tomography data is proposed using ML. Neuro-fuzzy modeling and ANN were used to diagnose BC. The algorithms were based on molecular biomarkers, including gene expression and methylation. The ML method based on images of cells obtained from urine samples of patients diagnosed with BC showed a diagnostic accuracy of 94%. DL in BC was used for accurate tumor typing based on their response to chemotherapy. Based on the results of deep machine learning, the molecular subtype of BC samples was predicted using histological examination. ML and DL algorithms for diagnosis, differential diagnosis, and prediction of recurrence and survival in kidney cancer were trained on CT texture analysis, genetic mutations, and Fuhrman nuclear grade. In addition to diagnosis, AI is used to optimize the treatment strategy for kidney cancer. In all cases, the ML, ANN, and DL algorithms improved the accuracy of diagnosis, survival assessment, and the effectiveness of pharmacological and surgical treatment of urologic malignancies.