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The role of the playwright is threatened by the technological advance of language generator software, including Chat GPT. New technology may be incorporated into the playwriting process but can undermine the essentially human work of the writer. Although today, Chat GPT is incapable of writing a human-standard play unaided, both the nominal and expressive authenticity of any play may be challenged in the future due to the impact of language generator software. Perhaps rejecting the software's use in playmaking is therefore the simplest solution in order to maintain the authenticity of the play and the playwrighting process. [ABSTRACT FROM AUTHOR]

The arrival of image-making generative artificial intelligence (AI) programs has been met with a broad rebuke: to many, it feels inherently wrong to regard images made using generative AI programs as artworks. I am skeptical of this sentiment, and in what follows I aim to demonstrate why. I suspect AI generated images can be considered artworks; more specifically, that generative AI programs are, in many cases, just another tool artists can use to realize their creative intent. I begin with an overview of how generative AI programs, like OpenAI's DALL-E 2, work. Then, leveraging work by Claire Anscomb, I argue that generative AI programs are a new technique of automatic image-making that affords creative agency to its users, thereby qualifying the images they create as artworks. Finally, I show many of the objections brought against AI artworks—including accusations of plagiarism and artistic devaluation—are due to the social backdrop in which we currently find them, rather than the technology itself. In the end, I aim to open the door to further aesthetic debate concerning AI generated images and art. [ABSTRACT FROM AUTHOR]

The article examine the copyrightability of art created with the assistance of artificial intelligence (AI) tools, particularly focusing on the role of randomness in AI-assisted creations and its connection to human creativity and copyright protection. It explores different arguments and perspectives on whether AI-generated art should be eligible for copyright protection. It further discusses the ongoing discourse on the intersection of AI, creativity, and intellectual property law.

Simple Summary: Artificial Intelligence (AI)-driven software that utilizes Computed Tomography (CT)images has the capability to automatically assess body composition and diagnose sarcopenia. Our research indicates that combining standardized CT staging methods with sarcopenia analysis could assist in identifying patients with advanced urothelial tumors who may benefit from customized nutritional therapies, ultimately resulting in improved outcomes and quality of life. The AI tool can represent a means to increase the clinical value of CT imaging reports and to promote the development of precision medicine. Background: Sarcopenia is a well know prognostic factor in oncology, influencing patients' quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes. Methods: We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints. Results: 97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10–20% up to ~45–55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle. Conclusions: A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in parallel. Several guidelines have provided reporting standards for publications of AI-based research in medicine and radiology. Yet, there is an unmet need for recommendations on the assessment of AI software before adoption and after commercialization. As the radiology AI ecosystem continues to grow and mature, a formalization of system assessment and evaluation is paramount to ensure patient safety, relevance and support to clinical workflows, and optimal allocation of limited AI development and validation resources before broader implementation into clinical practice. To fulfil these needs, we provide a glossary for AI software types, use cases and roles within the clinical workflow; list healthcare needs, key performance indicators and required information about software prior to assessment; and lay out examples of software performance metrics per software category. This conceptual framework is intended to streamline communication with the AI software industry and provide healthcare decision makers and radiologists with tools to assess the potential use of these software. The proposed software evaluation framework lays the foundation for a radiologist-led prospective validation network of radiology AI software. Learning Points: The rapid expansion of AI applications in radiology requires standardization of AI software specification, classification, and evaluation. The Canadian Association of Radiologists' AI Tech & Apps Working Group Proposes an AI Specification document format and supports the implementation of a clinical expert evaluation process for Radiology AI software. [ABSTRACT FROM AUTHOR]

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• Automatic lesion counting and volumetry is feasible with both 2D- and 3D- FLAIR images. • Automatic lesion counting and volumetry leads to significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSC NAIMS consensus recommendations. • Follow-up examinations should be performed on the same scanner with the same sequence parameters even with the use of AI-programs to obtain a valid statement of the disease course. Multiple Sclerosis (MS) is a common autoimmune inflammatory disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) allows a sensitive assessment of the CNS and is established for diagnostic, prognostic and (therapy-) monitoring purposes. Especially lesion counting in T2- or Fluid Attenuated Inversion Recovery (FLAIR)-weighted images plays a decisive role in clinical routine. Software-packages allowing an automatic evaluation of image data are increasingly established aiming a faster and improved workflow. These programs allow e.g. the counting, spatial attribution and volumetry of MS-lesions in FLAIR-weighted images. Research has shown that 3D-FLAIR-sequences are superior to 2D-FLAIR-sequences in visual evaluation of lesion burden in MS. An influence on the automatic analysis is expectable but not yet systematically studied. This work will therefore investigate the influence of 2D- and 3D datasets on the results of an automatic assessment. In this prospective study, 80 Multiple Sclerosis patients underwent a clinically indicated routine MRI examination. The clinical routine protocol already including a 3D-FLAIR sequence was adapted by an additional 2D-FLAIR sequence also conform to the 2021 MAGNIMS-CMSC NAIMS consensus recommendations. To obtain a quantitative analysis for assessment of amount, dissemination and volume of the lesions, the acquired MR images were post-processed using the CE-certified Software mdbrain (mediaire, Berlin, Germany). The resulting data were statistically analysed using the paired t -test for normally distributed data and the Wilcoxon-signed-rank-test for not normally distributed data respectively. Demographic data and data such as the subtype, duration, severity and therapy of the disease were collected, pseudonymized and evaluated. There is a significant difference concerning the total number and lesion volume with more lesions being detected (2D: 29.7, +/- 20.22 sd; 3D: 40.1 +/- 31.67 sd; p < 0.0001) but lower total volume (2D: 6.24 +/- 6.11 sd; 3D: 5.39 +/- 6.37 sd; p < 0.0001) when using the 3D- sequence. Especially significantly more small lesions in the unspecific white matter and infratentorial region were detected by using the 3D-FLAIR sequence (p < 0.0001) compared to the 2D-FLAIR image. Main reason for the lower total volume in the 3D-FLAIR sequence was the calculated volume for periventricular lesions which was significantly beneath the calculated volume from the 2D-FLAIR sequence (p < 0.0001). Automatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSC NAIMS consensus recommendations. This study contributes valuable insights into the impact of using different input data from the same patient for automated MS lesion evaluation. [ABSTRACT FROM AUTHOR]

Objective: To assess application value of artificial intelligence software DEEPVESSEL FFR(DV-FFR) in evaluating CTA (computed tomography angiography) of coronary ischemia. Methods: This was a prospective, single-center, and self-control study, and 21 vessels in 18 patients with coronary artery narrow (degree between 30%-90%) were included. Coronary ischemia was evaluated by DV-FFR and invasive FFR, and the efficiency of DV-FFR for diagnosing coronary ischemia was assessed by comparison with the results of golden criterion (invasive FFR). Based on data obtained from CTA DICOM, the blood vessel images were segmented and reconstructed by DV-FFR with deep learning technology, and vessel centerlines was obtained. FFR was calculated using3-D geometric auto-quantitative technology to assess ischemia in LAD (left anterior descending), LCX (left circumflex), RCA (right coronary artery). Results: When cut-off value of invasive FFR was taken as 0.8, DV-FFR≤0.8 was considered as functional coronary ischemia. On vessel level, the accuracy, specificity, sensitivity, positive predictive value, and negative predictive value of DV- FFR for diagnosing functional coronary ischemia were 90.5%, 88.9%, 91.7%, 91.7% and 88.9%, respectively. While on patient level, the above indexes were 88.9%, 87.5%, 90.0%, 90.0%and 87.5%, respectively. The results of the 2 methods were similar (P=0.787). Conclusions: The results of DV-FFR showed a good consistency with that of invasive FFR in evaluating coronary artery narrow degree between 30%-90%, which could be used as effective diagnostic approach.

Background: Sarcopenia is a well know prognostic factor in oncology, influencing patients’ quality of life and survival. We aimed to investigate the role of sarcopenia, assessed by a Computed Tomography (CT)-based artificial intelligence (AI)-powered-software, as a predictor of objective clinical benefit in advanced urothelial tumors and its correlations with oncological outcomes. Methods: We retrospectively searched patients with advanced urothelial tumors, treated with systemic platinum-based chemotherapy and an available total body CT, performed before and after therapy. An AI-powered software was applied to CT to obtain the Skeletal Muscle Index (SMI-L3), derived from the area of the psoas, long spine, and abdominal muscles, at the level of L3 on CT axial images. Logistic and Cox-regression modeling was implemented to explore the association of sarcopenic status and anthropometric features to the clinical benefit rate and survival endpoints. Results: 97 patients were included, 66 with bladder cancer and 31 with upper-tract urothelial carcinoma. Clinical benefit outcomes showed a linear positive association with all the observed body composition variables variations. The chances of not experiencing disease progression were positively associated with ∆_SMI-L3, ∆_psoas, and ∆_long spine muscle when they ranged from ~10–20% up to ~45–55%. Greater survival chances were matched by patients achieving a wider ∆_SMI-L3, ∆_abdominal and ∆_long spine muscle. Conclusions: A CT-based AI-powered software body composition and sarcopenia analysis provide prognostic assessments for objective clinical benefits and oncological outcomes.

The emergence of neural networking designs with great performance in computer tasks which are vision-related has sparked interest in radiology artificial intelligence (AI). Radiologists could get advantage from a greater understanding the principles of AI as AI-based software systems become more incorporated into the clinical workflow. Machine learning (ML) is becoming increasingly popular in the domains of medical imaging, radiomics, and medical image analysis. Deep learning is a sort of machine learning that originated in the field of computer vision and has since expanded in popularity across a wideaspect range of sectors. Deep learning has demonstrated outstanding performance in a variety of fields, including picture classification, object detection, and segmentation. This work gives an overall overview of recent achievements in this area by surveying deep learning architectures and DL approaches used to diagnose disease based on medical images. [ABSTRACT FROM AUTHOR]

Objectives: Fast diagnosis of Coronavirus Disease 2019 (COVID-19), and the detection of high-risk patients are crucial but challenging in the pandemic outbreak. The aim of this study was to evaluate if deep learning-based software correlates well with the generally accepted visual-based scoring for quantification of the lung injury to help radiologist in triage and monitoring of COVID-19 patients. Materials and methods: In this retrospective study, the lobar analysis of lung opacities (% opacities) by means of a prototype deep learning artificial intelligence (AI)-based software was compared to visual scoring. The visual scoring system used five categories (0: 0%, 1: 0–5%, 2: 5–25%, 3: 25–50%, 4: 50–75% and 5: >75% involvement). The total visual lung injury was obtained by the sum of the estimated grade of involvement of each lobe and divided by five. Results: The dataset consisted of 182 consecutive confirmed COVID-19 positive patients with a median age of 65 ± 16 years, including 110 (60%) men and 72 (40%) women. There was a correlation coefficient of 0.89 (p < 0.001) between the visual and the AI-based estimates of the severity of lung injury. Conclusion: The study indicates a very good correlation between the visual scoring and AI-based estimates of lung injury in COVID-19.

After a great deal of press and promise, artificial intelligence is beginning to make inroads in the sales function. In this paper, we follow one United Kingdom-based company called Cloudapps and the firm's development of an artificial intelligence solution to address the issues inherent in the management of the sales pipeline. We then outline how the use of this technology can help the firm both manage their pipeline as well as develop sustainable value propositions for their current and potential clients. In doing so we also point out the importance of gaining experience and using incremental compensation, in helping to gain the benefits that AIpotentially provides. [ABSTRACT FROM AUTHOR]

As Artificial Intelligence and Machine Learning continue to transform numerous aspects of our everyday lives, their role in the legal profession is growing in prominence. A subfield of AI with particular applicability to legal analysis is Natural Language Processing (NLP). NLP deals with computational techniques for processing human languages such as English, making it a natural tool for processing the text of statutes, regulations, judicial decisions, contracts, and other legal instruments. Paradoxically, although state-of-the-art Machine Learning and NLP algorithms are able to learn and act upon patterns too complex for humans to perceive, they nevertheless perform poorly on many cognitive tasks that humans routinely perform effortlessly. This profoundly limits the ability of AI to assist in many forms of legal analysis and legal decision making. This article offers two theses. First, notwithstanding impressive progress on NLP tasks in recent years, the state-of-the-art in NLP will remain unable to perform legal analysis for some time. Second, lawyers, legal scholars, and other domain experts can play an integral role in designing AI software that can partially automate legal analysis, overcoming some of the limitations in NLP capabilities. [ABSTRACT FROM AUTHOR]

We have created an automated kiosk that uses embodied intelligent agents to interview individuals and detect changes in arousal, behavior, and cognitive effort by using psychophysiological information systems. In this paper, we describe the system and propose a unique class of intelligent agents, which are described as Special Purpose Embodied Conversational Intelligence with Environmental Sensors(SPECIES). SPECIES agents use heterogeneous sensors to detect human physiology and behavior during interactions, and they affect their environment by influencing human behavior using various embodied states (i.e., gender and demeanor), messages, and recommendations. Based on the SPECIES paradigm, we present three studies that evaluate different portions of the model, and these studies are used as foundational research for the development of the automated kiosk. The first study evaluates human-computer interaction and how SPECIES agents can change perceptions of information systems by varying appearance and demeanor. Instantiations that had the agents embodied as males were perceived as more powerful, while female embodied agents were perceived as more likable. Similarly, smiling agents were perceived as more likable than neutral demeanor agents. The second study demonstrated that a single sensor measuring vocal pitch provides SPECIES with environmental awareness of human stress and deception. The final study ties the first two studies together and demonstrates an avatar-based kiosk that asks questions and measures the response using vocalic measurements. [ABSTRACT FROM AUTHOR]

In online shopping environments, the product-advising function originally performed by salespeople is being increasingly taken over by software-based product recommendation agents (PRAs). However, the literature has mostly focused on the functionality design and utilitarian value of such decision support systems, mostly ignoring the potential social influence they could exert on their users. The objective of this study is to apply a social relationship perspective to the design of interfaces for PRAs. We investigate the effects of applying anthropomorphic interfaces--namely, humanoid embodiment and voice output--on users' perceived social relationship with a technological and software-based artifact designed for electronic commerce contexts. The findings from a laboratory experiment indicate that using humanoid embodiment and human voice--based communication significantly influences users' perceptions of social presence, which in turn enhances users' trusting beliefs, perceptions of enjoyment, and ultimately, their intentions to use the agent as a decision aid. These results extend the applicability of theories concerning traditional shopper--salesperson relationships to customers' interactions with technological artifacts residing on Web sites--that is, the recommendation agent software--and provide practitioners with guidelines on how to design Internet stores with the goal of building social relationships with online shoppers and enhancing their overall shopping experiences. [ABSTRACT FROM AUTHOR]

Both feature recognition and reasoning are needed for automating manufacturing planning activities for 3D sheet metal components. The feature reasoning system proposed here generates manufacturing information (e.g. flat pattern, locations of various features, internal cuts and blank profiles, type of tools required, operation sequence, bending sequence, etc.) for the features recognized in Part I of this paper. The issues related to flat pattern developments as experienced while using commercial software have been addressed in this work. A tool selection approach that uses standard tools for single and multiple blow features in punch presses is suggested. A zigzag method for operation sequencing and a virtual bend sequencing system are included. The system has been tested with various components and the results are compatible with industrial practices. [ABSTRACT FROM AUTHOR]

The application of intelligent software agents to the current and ever-growing investment management problem facing millions of participants with billions of dollars of assets in defined-contribution plans is presented. The underlying events that triggered this challenge are presented, followed by an introduction to intelligent software agents. An approach is suggested as to how this technology might be employed to assist plan participants in accumulating, assimilating and analyzing quickly changing volumes of information relevant to their investment portfolios and then providing advice in the form of ranked suggestions. The ultimate goal is to aid investors in achieving personal short- and long-term retirement goals and maximizing their portfolio's return while dealing with government and plan regulations that may conflict with and/or restrict their desired outcome. [ABSTRACT FROM AUTHOR]

Life stress is a well-established risk factor for a variety of mental and physical health problems, including anxiety disorders, depression, chronic pain, heart disease, asthma, autoimmune diseases, and neurodegenerative disorders. The purpose of this article is to describe emerging approaches for assessing stress using speech, which we do by reviewing the methodological advantages of these digital health tools, and the validation, ethical, and privacy issues raised by these technologies. As we describe, it is now possible to assess stress via the speech signal using smartphones and smart speakers that employ software programs and artificial intelligence to analyze several features of speech and speech acoustics, including pitch, jitter, energy, rate, and length and number of pauses. Because these digital devices are ubiquitous, we can now assess individuals' stress levels in real time in almost any natural environment in which people speak. These technologies thus have great potential for advancing digital health initiatives that involve continuously monitoring changes in psychosocial functioning and disease risk over time. However, speech-based indices of stress have yet to be well-validated against stress biomarkers (e.g., cortisol, cytokines) that predict disease risk. In addition, acquiring speech samples raises the possibility that conversations intended to be private could one day be made public; moreover, obtaining real-time psychosocial risk information prompts ethical questions regarding how these data should be used for medical, commercial, and personal purposes. Although assessing stress using speech thus has enormous potential, there are critical validation, privacy, and ethical issues that must be addressed. [ABSTRACT FROM AUTHOR]

Telemedicine is a promising strategy that utilizes telecommunication to provide health care in remote areas, facilitating beneficial interaction between the health care provider and people in rural areas and making affordable and accessible medical care available to remote, inaccessible areas of the world. This article provides an overview of some of the ways telemedicine is improving diabetes care outcomes at the community level. Telemedicine can play a number of roles in moving quality diabetes care forward. It is currently being used to create awareness among urban and rural population about the risk factors and prevention of diabetes; to facilitate patient monitoring; for remote diabetic retinopathy screening; and in diabetes prevention at the primary, secondary, and tertiary level. We also highlight the use of automated artificial intelligence software combined with telemedicine to conduct efficient real-time screening of complications such as diabetic retinopathy in remote areas where such facilities are currently unavailable. [ABSTRACT FROM AUTHOR]

The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data. [ABSTRACT FROM AUTHOR]

The article presents a case study on the MITA ChatBot system, an artificial intelligence (AI) conversation software, launched by the telecom company Banglalink Digital Communications. It highlights the impact of the MITA on improvement in customer care experiences. It also discusses the features and structure of MITA for product information, customer satisfaction and conversation modelling.

Research on multi-objective optimization (MO) has become one of the hot points of intelligent computation. In this paper, an archive-based multi-objective artificial bee colony optimization algorithm (AMOABC) is proposed, in which an external archive is used to preserve the current obtained non-dominated best solutions, and a novel Pareto local search mechanism is designed and incorporated into the optimization process. To prevent the searching process from being trapped into local minimum, a novel food source generating mechanism is put forward, and different search strategies are designed for bees and local search process. Comprehensive benchmarking and comparison of AMOABC with the some current-related MO algorithms demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

What if regulators and law enforcement agencies could mandate not just non-deceptiveness but also forthrightness from the software products and services that have begun to run our daily lives? Forthrightness would obligate companies to be completely honest, direct, and candid. Importantly, forthrightness would impose an affirmative obligation to warn rather than a passive obligation to inform. A forthright company will anticipate what a consumer does not understand because of cognitive biases, information overload, or other mechanisms that interfere with information comprehension, and will be obligated to communicate important information in a way that overcomes these barriers. Forthrightness will prove especially important to address looming new challenges from artificial intelligence ("AI"). Unlike calls for fairness or transparency, forthrightness for AI will focus on the actions of the entity developing or using the AI system, as opposed to describing a desirable attribute of the system itself. Forthrightness will arise most importantly when AI systems make egregious mistakes. In these moments of failure and potential crisis, the forthright actor will try to fix the fundamental problem plaguing the system rather than hide the problem beneath a thin veneer of obfuscation. [ABSTRACT FROM AUTHOR]