For data science tools to mature and become integrated into routine clinical practice, they must add value to patient care by improving quality without increasing cost, by reducing cost without changing quality, or by both reducing cost and improving quality. Artificial intelligence (AI) algorithms have potential to augment data-driven quality improvement for radiologists. If AI tools are adopted with population health goals in mind, the structure of value-based payment models will serve as a framework for reimbursement of AI that does not exist in the fee-for-service system.
Artificial intelligence (AI) is a prominent tool that enables people to rethink how they consolidate information, analyze data, and use the observations to improve decision making, and it is already revolutionizing every walk of life. The objective of AI is to model human intellectual functions. It is causing a fundamental change in healthcare, thanks to the growing availability of healthcare data and the rapid advancement of analytics techniques. The healthcare market for AI is rapidly increasing at a rate of 40%, and by the end of 2021, it is expected to reach $6.6 billion. Deep neural networks, natural language processing, computer vision, and robotics have all made significant advances in artificial intelligence (AI) in recent years. These techniques are already being used in healthcare, with AI anticipated to take over many of the tasks currently performed by clinicians and administrators in the future. Patient administration, clinical decision support, patient monitoring, and healthcare treatments are the four primary areas where AI will have the largest impact. Many elements of patient care, as well as administrative operations inside providers, payers, and pharmaceutical companies, could be 58transformed by these technologies. The approach to medicine is progressing with the advancement of new (AI) methods of machine learning. Conjoined with rapid improvements in computer processing, these AI-based systems are already enhancing the accuracy and efficiency of diagnosis and treatment across various specializations. The developing focus of AI in radiology has led some experts to suggest that someday AI may even substitute radiologists. A number of studies have already shown that AI can perform as well as or better than humans at crucial healthcare activities like disease diagnosis. Algorithms are already surpassing radiologists in terms of detecting dangerous tumors and advising researchers on how to build cohorts for expensive clinical trials. However, we believe it will be several years before AI replaces humans in large medical process domains for a variety of reasons. Unquestionably, AI is the most considered issue today in medical imaging research, both in diagnostic and therapeutic areas. Scientists have enforced AI to automatically analyze complex patterns in imaging data and help in quantitative assessments of radiographic characteristics. In radiation oncology, AI has been applied to different image procedures that are used at different stages of the treatment, i.e., tumor declination and treatment assessment. For example, AI is essential for boosting power for processing a huge number of medical images and therefore brings to light disease characteristics that are not seen by the naked eye. The utilization of AI within the diagnostic process aiding medical specialists could be of great potential for the healthcare sector and the overall patient’s well-being. The assimilation of AI into the current technical framework stimulates the identification of relevant medical data from multiple sources, which is tailored to the needs of the patient and the treatment process. Simultaneously, AI unchains silo thinking, such as sharing knowledge across departmental boundaries, as information from all involved areas is taken into account. Furthermore, AI develops results based on a larger community rather than on subjective experiences and achieves equal outcomes when using similar medical data and does not depend on situations, emotions, or time of day.
This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.
There is a variety of definitions about Artificial intelligence (AI). In this paper, the following definition is selected advisedly: AI is the development of computer systems to solve difficult problems, which can not be solved by an exhaustive examination of all possible solutions since these may be too many. From this point of view, as this definition guides, a brief review of some of AI-tools is attempted with two targets: To present some of the tools (older-“classics” and younger-“exotics”) and to explore their penetrating potentiality in the opportune fields of Geodesy and Geomatics.
We describe an AI modeling tool meant to be used by both designers and developers. The method for authoring is visual and meant to convey decision logic in a more intuitive manner while retaining expressiveness. This data-driven approach features an AI runtime engine which incorporates several augmentations which make it suitable for use across a wide array of deployed systems.
An in-depth description and analysis of some of the most important tools and techniques that are available to the professional artificial intelligence programmer, researcher, or student are presented in this text The focus is primarily, but not exclusively, on expert system development tools, particularly those that have been in use in the USA and other countries for several years as well as some newer tools or extensions to existing tools These tools are primarily rule-based and frame-based systems with support for functional object-oriented, and access-oriented programming paradigms Some of the tools are only available in the research community, but many are commercial tools that are currently used for both research and product development
An expert system shell is developed for a personal computer using C language. It is designed for diagnosis problems such as fault diagnosis and process operation guidance. The tool is suitable for the knowledge that is represented in the form of the hierarchical tree and causal relationship matrix. Further by using the tool it is easy to build expert systems which cooperatively work with the existing software by passing or receiving data, and which evokes or is evoked by external programs. Therefore the tool is appropriate to construct on-line expert systems. Application examples are shown to prove the usefulness of the tool.
that utilize artificial intelligence. Chung proposes for AI-powered tools to effectively support artistic processes, they must seamlessly integrate into existing workflows and enable the expression of subtle intentions through gradual changes. For Chung, designers hold, therefore, a crucial role, as these tools will influence whether such technologies benefit or harm society. Professor Lydia Chilton’s examines the use of AI in the design process, which provides further insights into this topic. She analyzes both the successes and failures of designing with AI and concludes that, while AI can be helpful, it requires human guidance to truly become a powerful creative tool. To reach this conclusion, she shares her journey in demystifying the “magic” of the design process and the role generative AI systems can play in that process. Chilton describes neither of these is magical, but instead requires work to execute or understand. She identifies the “flare” portion of the design process as a place where AI can support the design process by providing access to a wide range of inspiration. T he advent of machine learning models, such as DALL-E, Midjourney, and ChatGPT, has prompted artists and researchers to revisit the concept of creativity. As with any new technology being introduced in practice, reactions diverge, ranging from the enthusiastic embrace of new possibilities to apprehensive avoidance. This current time exacerbated by social media echoes Walter Benjamin’s criticisms regarding reproduction technologies such as photography and film [1]. With the democratization of art-making through these technologies, will art lose its “aura,” its uniqueness? Of course, it won’t! Art is founded on human expressivity and spirituality and its value is ultimately validated through its potential to create emotional connections between artists and audiences. While tools like machine learning models can certainly support artistic expressiveness, art transcends mere tool usage and remains a testament to human creativity. In this issue, we explore the various ways in which computation can assist in creating media, art, design, and craft. With our selection of authors, we wished to encompass a broad spectrum of computational support that ranges from practitioners manually creating most of the outcome with light forms of computer automation to practitioners minimally guiding the outcome with fully generative computational processes. To examine this spectrum, we have brought together artists, designers, and researchers with diverse expertise to discuss their respective practices, spanning digital fabrication to support craft practices, human-robot collaborative performance, and AIgenerated visual design. The objective is to present perspectives acknowledging both the potential advantages and drawbacks of integrating automation to support creativity. These perspectives are presented with a focus on understanding how artists and researchers define their interactions with computers and how these interactions affect their creative process. Specifically, these articles will investigate the different roles taken by computational technologies within this creative spectrum: a tool for reducing tedium, a guide for exploring a creative space, a creative medium for enabling new forms of art, a collaborator with its own intentions, or a nuanced combination of these different roles. Our investigation starts with discussing the role and implication of AI for creativity support. Our first article, authored by Dr. John Chung, draws parallels between AI-powered tools for art production and the introduction of technology (AI or not) in creative domains. They democratize both the distribution and production of creative outputs. He further delves into the crucial role of designers of creativity-support tools Exploring the Horizon of Computation for Creativity
The digitalisation of the industry offers new opportunities to discuss design activities and support tools. Advancement in AI allows thinking about new Designer-AI tools interaction in the design process. The paper aims to initiate a characterisation of tools issued from researches in the application of AI in Design to rethink the division of work between Designer-AI tools. The paper is based on the literature on the concept of Levels of Automation in cognitive engineering, manufacturing and robotics, and proposes a grid of characterisation of the Level of Automation for the design process.
In recent years, the healthcare industry has witnessed a revolutionary shift driven by advances in artificial intelligence (AI) and machine learning (ML) technologies. These groundbreaking tools are transforming the way we deliver care, enhance patient outcomes, and optimize healthcare systems. The role of AI and ML in healthcare has become increasingly prominent, opening up new avenues for innovation, precision medicine, and improved decision-making. As we embark on this transformative journey, it is crucial to explore the potential, challenges, and ethical implications of integrating AI and ML into healthcare.
One of the key areas where AI and ML have shown immense promise is in the realm of diagnostics. By analyzing vast amounts of medical data, these technologies can quickly and accurately detect patterns, identify anomalies, and assist in diagnosing diseases. AI-powered algorithms can analyze medical images, such as X-rays and MRIs, with remarkable accuracy, enabling early detection of diseases like cancer and improving patient outcomes. ML algorithms can also assist healthcare professionals in predicting disease progression, guiding treatment plans, and offering personalized medicine approaches, leading to more targeted and effective interventions.
Moreover, AI and ML have the potential to revolutionize healthcare delivery and management. Intelligent systems can optimize hospital operations, streamline administrative tasks, and enhance resource allocation. From scheduling appointments and managing electronic health records to predicting patient flow and optimizing bed occupancy, these technologies can help healthcare organizations work more efficiently, reduce costs, and improve patient experiences. Furthermore, AI-powered chatbots and virtual assistants can provide personalized health advice, answer patient queries, and offer triage support, enhancing accessibility and patient engagement.
However, as we embrace the potential of AI and ML in healthcare, it is essential to address several challenges and ethical considerations. Data privacy and security, algorithmic biases, and the potential for AI to replace human judgment are critical concerns. Striking the right balance between human expertise and machine assistance is crucial to ensure that patient-centric care remains at the forefront. Rigorous validation, robust regulatory frameworks, and ongoing monitoring are necessary to ensure the safety, efficacy, and ethical use of AI and ML in healthcare.
In conclusion, the role of artificial intelligence and machine learning in healthcare is transformative, promising significant advancements in diagnostics, healthcare delivery, and patient outcomes. These technologies have the potential to revolutionize how we approach healthcare, enhance decision-making, and improve resource allocation. However, it is vital to navigate the challenges and ethical considerations associated with AI and ML adoption. By embracing a collaborative approach that combines human expertise with intelligent systems, we can harness the full potential of these technologies and create a future where AI and ML empower healthcare professionals, improve patient experiences, and contribute to healthier communities
The historical origin of the Artificial Intelligence (AI) is usually established in the Dartmouth Conference, of 1956. But we can find many more arcane origins [1]. Also, we can consider, in more recent times, very great thinkers, as Janos Neumann (then, John von Neumann, arrived in USA), Norbert Wiener, Alan Mathison Turing, or Lofti Zadeh, for instance [12, 14]. Frequently AI requires Logic. But its Classical version shows too many insufficiencies. So, it was necessary to introduce more sophisticated tools, as Fuzzy Logic, Modal Logic, Non-Monotonic Logic and so on [1, 2]. Among the things that AI needs to represent are categories, objects, properties, relations between objects, situations, states, time, events, causes and effects, knowledge about knowledge, and so on. The problems in AI can be classified in two general types [3, 5], search problems and representation problems. On this last "peak", there exist different ways to reach their summit. So, we have [4] Logics, Rules, Frames, Associative Nets, Scripts, and so on, many times connected among them. We attempt, in this paper, a panoramic vision of the scope of application of such representation methods in AI. The two more disputable questions of both modern philosophy of mind and AI will be perhaps the Turing Test and the Chinese Room Argument. To elucidate these very difficult questions, see our final note.
Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.
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