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Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

414 LANGUAGE, VOLUME 67, NUMBER 2 (1991) Brixhe & Lejeune's 1984 Corpus des inscriptions paléo-phrygiennes. Since Phrygian is both closely related to Greek and preserved in part through Greek testimony, N has chosen to present his account as a history of PhrygianGreek linguistic contact. This procedure may explain his vagueness in discussing shared features of the two languages: although he calls them 'common innovations' (10), he seems elsewhere to regard them as the result of borrowing during a hypothesized period of close contact among Greek, Phrygian, and several other IE languages in the south Balkans (5-6). Some of these shared features are quite striking—e.g. factitives in *-oyô like Gk. orthöö 'straighten'. Phr. kakoioi (11, 13), and laryngeal vocalization followed by umlaut in Gk. ónoma, Phr. onoman = [b] not [bh], this development is trivial and proves only that Phrygian did not share the Greek change to [ph] etc.). Pp. 6-10: Common retentions of Greek and Phrygian (e.g. the augment and the thirdperson present middle ending -toi). Pp. 10-13: Common innovations of Greek and Phrygian (e.g. agent nouns in -tä, Gk. autos 'self = Phr. autos 'particular'). Pp. 13-16: Inherited lexical material of Phrygian which is lost or only marginally preserved in Greek. Pp. 16-18: Sometime after the hypothesized period of south Balkan close contact with Greek, the Phrygians migrated to Anatolia, where they are attested in historical times. Contact with speakers ofAnatolian languages led to borrowings into Phrygian (mainly names, e.g. Midas). Pp. 18-19: Greek expansion in the first millennium led to contact and hence Greek loans into Phrygian. Pp. 19-22: Direct attestation of Phrygian lost from the third century B.C.E. through the first century CE. Pp. 22-23: Neo-Phrygian attested in grave inscriptions, still in part uninterpretable . Although Phrygian is unlikely ever to play a significant role in IE historical linguistics, it does, as N remarks in conclusion (23), repay time spent applying to it our knowledge of IE. N's essay is a valuable contribution to the project of understanding Phrygian. [Andrew Garrett, University of Texas, Austin.] Natural language processing technologies in Artificial Intelligence: The science and industry perspective. By Klaus K. Obermeier. Chichester : Ellis Horwood, 1989. Pp. 263. Natural language processing (NLP) is the area of research concerned with the many aspects of 'natural' language communication with computers . In this book Obermeier provides an introduction to NLP from both commercial and academic perspectives, primarily aimed at businessmen and data-processing professionals. His presentation is nontechnical, though very comprehensive . Linguists will find the book an accessible introduction to the practical issues in NLP, which may help to clarify the direction and emphasis in this area of language research. The book's organization is excellent, making it very easy to read, although the typographic and editorial quality are not the best. The twelve chapters are divided into three sections: 'NLP in perspective' (17-92). which provides an introduction to the structure of NLP systems and the approaches and formalisms used to implement them; 'Applications' (93-185), in which six major applications of NLP are described: and 'Bottom line' (187-232), where O presents his own evaluation of the state of NLP research and current systems. A good list of references (239-59) is also included. Much of the material in the first section will be familiar to linguists, since the design of NLP systems is largely based on formal linguistic analyses in the areas ofphonology, morphology, syntax, and semantics. In addition, methods for knowledge representation and reasoning drawn from Artificial Intelligence are explained in relation to their relevance to NLP development. This nontechnical overview of the techniques used in NLP is up-to-date. It includes a discussion of parsing methods ranging from contextfree parsing, unification-based parsing, and ATNs to such knowledge-representation schemes from AI as production rules, semantic networks, and frames, to the new connectionist models. BOOK NOTICES 415 The second section comprises the largest part of the book, with one chapter devoted to each of the applications addressed: natural-language interfaces, machine translation, text processing/ understanding, text generation, speech processing , and writing aids. For each application there is a brief...

BackgroundModernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent medical universities are actively conducting research and exploration in this area. In particular, given the shortage of human resources, the need to maintain social distancing to prevent the spread of the epidemics, and the increase in the cost of medical education, it is critical to harness online learning to promote medical education. A virtual case learning system that uses natural language processing technology to process and present a hospital’s real medical records and evaluate student responses can effectively improve medical students’ clinical thinking abilities.ObjectiveThe purpose of this study is to develop a virtual case system, AIteach, based on actual complete hospital medical records and natural language processing technology, and achieve clinical thinking ability improvement through a contactless, self-service, trial-and-error system application.MethodsCase extraction is performed on a hospital’s case data center and the best-matching cases are produced through natural language processing, word segmentation, synonym conversion, and sorting. A standard clinical questioning data module, virtual case data module, and student learning difficulty module are established to achieve simulation. Students can view the objective examination and inspection data of actual cases, including details of the consultation and physical examination, and automatically provide their learning response via a multi-dimensional evaluation system. In order to assess the changes in students’ clinical thinking after using AIteach, 15 medical graduate students were subjected to two simulation tests before and after learning through the virtual case system. The tests, which included the full-process case examination of cases having the same difficulty level, examined core clinical thinking test points such as consultation, physical examination, and disposal, and generated multi-dimensional evaluation indicators (rigor, logic, system, agility, and knowledge expansion). Thus, a complete and credible evaluation system is developed.ResultsThe AIteach system used an internal and external double-cycle learning model. Students collect case information through online inquiries, physical examinations, and other means, analyze the information for feedback verification, and generate their detailed multi-dimensional clinical thinking after learning. The feedback report can be evaluated and its knowledge gaps analyzed. Such learning based on real cases is in line with traditional methods of disease diagnosis and treatment, and addresses the practical difficulties in reflecting actual disease progression while keeping pace with recent research. Test results regarding short-term learning showed that the average score (P < 0.01) increased from 69.87 to 85.6, the five indicators of clinical thinking evaluation improved, and there was obvious logical improvement, reaching 47%.ConclusionBy combining real cases and natural language processing technology, AIteach can provide medical students (including undergraduates and postgraduates) with an online learning tool for clinical thinking training. Virtual case learning helps students to cultivate clinical thinking abilities even in the absence of clinical tutor, such as during pandemics or natural disasters.

Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

Natural language processing (NLP) technology is an important constituent of artificial intelligence, focusing on the interaction between computers and human natural language, with the aim of enabling computers to understand, analyze, generate and process human languages. The fields of legal aid and judicial practice are also strongly related to texts, therefore, natural language processing technology has shown great potential in the legal field nowadays. In this study, the author reviews the development status, core technologies, application areas, and application value of natural language processing technology in the fields of legal aid and judicial practice. In addition, the study summarizes the difficulties and challenges faced by natural language processing technology in the field of legal aid and judicial practice. In particular, the current legal natural language processing technology still has technical problems, such as difficulty in processing complex and long legal texts, a lack of certain logical reasoning ability, insufficient understanding of non-professional questions, and a lack of public datasets, which leads to high model training costs, etc. In response to these problems, the study points out the need to make natural language processing models more adaptable to legal language, and proposes suggestions such as enhancing the popularity of models and open datasets.

This paper discusses machine learning techniques in biotechnology for analyzing protein structure and extracting features based on Natural language processing techniques in Artificial intelligence. Modern biotechnology is providing breakthroughs in products and technologies to address debilitating and rare diseases, thereby contributing to global healing. The convergence of artificial intelligence and biotechnology has emerged as a transformative force in the life sciences, enabling researchers to leverage vast amounts of data and derive meaningful insights. In the context of environmental biotechnology, real-time data processing facilitated by natural language processing is becoming increasingly crucial. The paper explains details about Protein structure is a fascinating and complex topic, essential for understanding how proteins function and different AI techniques for extracting key features associated with specific protein structures. This paper also talks about ethical Implications of AI in Biotechnology Data Privacy and Security Issues and Addressing Bias in Machine Learning Models

Blood transfusions are vital in health care, yet maintaining an adequate and safe blood supply remains a significant challenge. To address blood donation-associated challenges, this review explores how integrating artificial intelligence (AI) technologies can improve donor recruitment, retention and management. For instance, robotic process automation can streamline repetitive administrative tasks, allowing staff to focus on more critical activities and improving efficiency. When augmented with AI techniques such as machine learning (ML) and natural language processing, it transitions from static rule-based automation to intelligent process automation. This combination enables dynamic decision making, handling unstructured data and optimizing workflows, thus extending its role in improving efficiency and decision making in donor management. ML algorithms can analyse large datasets to predict future donation patterns, identify donor behaviour trends and forecast blood demand more accurately. By applying these predictive models, blood banks can plan more effectively, avoid shortages and precisely target recruitment efforts. Additionally, AI-driven chatbots are gaining traction as a tool for improving communication with potential and existing donors, ultimately fostering better retention rates. Beyond routine donor management, AI also shows promise in supporting rare donor identification and targeted engagement strategies. While these innovations hold great potential, their implementation faces challenges such as data availability and quality, ethical issues concerning AI utilization, the necessity for clinical and technical expertise, a robust infrastructure, environmental impact and cybersecurity risks. Addressing these issues through practical strategies and thoughtful integration will be the key to ensuring the responsible, effective and sustainable adoption of AI in blood banking systems.

This research aims to evaluate the possible positive aspects of Artificial Intelligence (AI) technologies in English language teaching that occur when training learners of English as a Foreign Language (EFL) or English as a Second Language (ESL). The study seeks to understand how AI technology and methodologies enhance language learning and teaching, including the unique possibilities that arise when considering learners and educators with an evolving role in increasingly larger education systems and educational environments. It seeks to understand the potential of AI for personalizing English language training. Also, by asking the question study, if there are methods including Natural Language Processing (NLP), Machine Learning (ML), and AI-based analytics or tools, can they personalize learning experiences for EFL-ESL learners? A qualitative study was completed through interviews with learners from different experience levels engaged in EFL or ESL programs that used AI-based tools in the pedagogy. The results demonstrate that AI has positive opportunities for personalized language education in the EFL and ESL contexts, the ability to enhance language skills in specific domains, and offer customized language learning experiences. This research has indicated challenges that surround cultural elements, technical challenges, and distinctly human teaching and learning elements. The research also offers helpful information for English language learning and recognizes the shifting nature of personal learning experience whether enhanced through AI, the practical requirement of incorporating AI will also provide a unique lens and perspective in English language learning and teaching, recognize the affordances AI can offer, alongside students and language learning never being exclusively cultural or human.

Abstract: Artificial intelligence (AI), which has sped up the creation of intelligent conversational systems, or chatbots, has totally changed how people interact with computers. This investigation looks at how AI-powered chatbots are developed, designed, and used in a variety of sectors, such as customer service, healthcare, education, and e-commerce. It highlights how natural language processing (NLP), machine learning (ML), and deep learning techniques are combined to assist chatbots in understanding, interpreting, and effectively responding to consumer requests. The research examines typical frameworks and platforms for chatbot development in addition to language comprehension, emotional intelligence, privacy, and user engagement challenges. Furthermore, the effectiveness and adaptability of chatbots have significantly increased due to recent advancements in generative AI models, such as GPT and BERT. The final section of this paper examines future research directions, emphasizing AI chatbots' potential to create conversational agents that are more human-like, contextually aware, and emotionally intelligent. With the creation of intelligent chatbots that can comprehend and react to natural language, artificial intelligence (AI) has completely changed human–computer interaction. The design and deployment of an AI-based chatbot that mimics human speech in order to deliver prompt, precise, and context-aware responses is the main emphasis of this study. The chatbot interprets user inquiries, extracts pertinent data, and provides insightful responses in real time by utilizing Natural Language Processing (NLP) and Machine Learning (ML) techniques. The solution improves user engagement and conversational accuracy by incorporating deep learning models for entity recognition and intent detection. Additionally, the chatbot's design includes modules for response generation, dialogue management, and data preparation to guarantee dependability and flexibility across a range of disciplines, especially in healthcare applications..

This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.

Aim: The purpose of this study was to investigate if principles of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), could be applied to the personal statements of general surgery residency applicants in order to gain valuable insight into the candidates and facilitate a more comprehensive assessment. Methods: The personal statements from individuals applying for a general surgery residency position during the 2021/22 application cycle (n = 1792) were analyzed using AI technology. Comparison groups were drawn from a database of documents from the general population and the personal statements of current general surgery residents (n = 64) at a single academic center. The study was conducted in collaboration with a leading language psychology and natural language processing organization. Results: Applicants exhibited a language-based personality that was highly self-assured (P &lt; 0.0001) and trusting (P &lt; 0.0001), and less stress-prone (P &lt; 0.0001) and impulsive (P &lt; 0.0001) than that of the general population. Compared to the general applicant pool, current residents were significantly more emotionally aware (P &lt; 0.001) and organized (P &lt; 0.001) and less self-assured (P &lt; 0.001) and less driven by power (P &lt; 0.001). Conclusion: Natural language processing technology can be utilized to assess the unique characteristics of general surgery resident applicants based on the content of their personal statements. In addition, candidates who successfully gain admission to a single academic program display different language-based personalities and drives compared to the general applicant pool. Incorporating these principles of artificial intelligence into the residency selection process could facilitate a more holistic evaluation of candidates.

In today’s digital world, phishing attacks have become one of the most serious cybersecurity threats. Cybercriminals use fake emails, messages, and websites to trick users into sharing sensitive information such as passwords, bank details, and personal data. Traditional phishing detection methods are often unable to detect advanced and evolving attacks effectively. Artificial Intelligence (AI) combined with Natural Language Processing (NLP) provides a powerful solution to detect phishing attacks. NLP helps in analyzing the textual content of emails and messages to identify suspicious patterns, keywords, and malicious intent. AI models such as machine learning and deep learning improve detection accuracy by learning from large datasets. This research paper focuses on the use of AI and NLP techniques for phishing detection. It explains the working process, methodologies, advantages, and challenges of implementing such systems. The study also highlights how these technologies can improve cybersecurity and protect users from online fraud. KEYWORDS Phishing Detection, Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning, Cybersecurity, Email Filtering, Text Classification