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Indian Journal of Pathology &... May 2022Machine learning and artificial intelligence (AI) have become a part of our daily routine. There are very few of us who are not influenced by this technology. There are... (Review)
Review
Machine learning and artificial intelligence (AI) have become a part of our daily routine. There are very few of us who are not influenced by this technology. There are a lot of misconceptions about the scope, utility, and fallacies of AI. Digital neuropathology is an evolving area of research. The importance of digital image processing stems from the rapid gains in computer vision and image processing that have happened in the past decade thanks to advancements in deep learning (DL). The article attempts to present to the audience a simple presentation of the technology and attempts to provide a context-based understanding of the DL process for image processing. Also highlighted are current challenges and the roadblocks in adopting the technology in routine neuropathology.
Topics: Artificial Intelligence; Humans; Image Processing, Computer-Assisted; Machine Learning; Neural Networks, Computer
PubMed: 35562153
DOI: 10.4103/ijpm.ijpm_115_22 -
Frontiers of Medicine Aug 2020Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image... (Review)
Review
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
Topics: Artificial Intelligence; Humans; Machine Learning; Neoplasms; Robotics
PubMed: 32728877
DOI: 10.1007/s11684-020-0761-1 -
Current Opinion in Obstetrics &... Aug 2022Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing... (Review)
Review
PURPOSE OF REVIEW
Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings.
RECENT FINDINGS
Artificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.
SUMMARY
In this review, we discuss various applications of artificial intelligence in different areas of reproductive medicine. We summarize the current findings with their potentials and limitations, and also discuss the future direction for research and clinical applications.
Topics: Artificial Intelligence; Female; Humans; Infertility; Machine Learning; Pregnancy; Reproductive Medicine
PubMed: 35895955
DOI: 10.1097/GCO.0000000000000796 -
Sensors (Basel, Switzerland) Aug 2021Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around... (Review)
Review
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.
Topics: Artificial Intelligence; Endoscopy; Humans; Machine Learning; Natural Language Processing; Robotics
PubMed: 34450976
DOI: 10.3390/s21165526 -
Kidney International Jul 2020Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on... (Review)
Review
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.
Topics: Artificial Intelligence; Machine Learning; Neural Networks, Computer; Reproducibility of Results; Software
PubMed: 32475607
DOI: 10.1016/j.kint.2020.02.027 -
Journal of Stomatology, Oral and... Jun 2022Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the... (Review)
Review
Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of AI in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by AI and to review the current applications of AI for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of AI in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with AI algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, AI algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted.
Topics: Algorithms; Artificial Intelligence; Humans; Machine Learning; Neural Networks, Computer
PubMed: 35091121
DOI: 10.1016/j.jormas.2022.01.010 -
Clinics in Geriatric Medicine Aug 2020Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus.... (Review)
Review
Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus. Diabetes represents a trove of physiologic and sociologic data that are only superficially understood by the health care system. Artificial intelligence can address many problems posed by the prevalence of diabetes mellitus and the impact of diabetes on individual and societal health. We provide a brief overview of artificial intelligence and discuss case studies that illustrate how artificial intelligence can enhance diabetes care.
Topics: Artificial Intelligence; Decision Making, Computer-Assisted; Decision Support Systems, Clinical; Delivery of Health Care; Diabetes Mellitus; Expert Systems; Humans; Knowledge Bases; Natural Language Processing; Neural Networks, Computer
PubMed: 32586478
DOI: 10.1016/j.cger.2020.04.009 -
Seminars in Ophthalmology Apr 2023Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using... (Review)
Review
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
Topics: Humans; Artificial Intelligence; Diabetic Retinopathy; Machine Learning; Neural Networks, Computer; Ophthalmology
PubMed: 36356300
DOI: 10.1080/08820538.2022.2139625 -
The New England Journal of Medicine May 2024
Review
Topics: Humans; Artificial Intelligence; Social Values; Language; Clinical Decision-Making; Risk; Clinical Reasoning; Bias
PubMed: 38810186
DOI: 10.1056/NEJMra2214183 -
Critical Care Clinics Jan 2023In recent years, the volume of digitalized web-based information utilizing modern computer-based technology for data storage, processing, and analysis has grown rapidly.... (Review)
Review
In recent years, the volume of digitalized web-based information utilizing modern computer-based technology for data storage, processing, and analysis has grown rapidly. Humans can process a limited number of variables at any given time. Thus, the deluge of clinically useful information in the intensive care unit environment remains untapped. Innovations in machine learning technology with the development of deep neural networks and efficient, cost-effective data archival systems have provided the infrastructure to apply artificial intelligence on big data for determination of clinical events and outcomes. Here, we introduce a few computer-based technologies that have been tested across these domains.
Topics: Humans; Artificial Intelligence; Big Data; Data Science; Neural Networks, Computer; Machine Learning
PubMed: 36333034
DOI: 10.1016/j.ccc.2022.07.008