Large Language Models For Healthcare

  large language models for healthcare: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
  large language models for healthcare: Artificial Intelligence in Medicine David Riaño, Szymon Wilk, Annette ten Teije, 2019-06-19 This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
  large language models for healthcare: Improving Healthcare Quality and Patient Engagement: Management and Technology Insights Chaturvedi, Vijit, Singh, Prashant, Ramachandran, Anandhi, Aggarwal, Divya, 2024-09-27 Enhancing healthcare quality and fostering patient engagement are pivotal in healthcare management. As global healthcare systems face challenges, from rising costs to various patient outcomes, innovations in technology transform patient care techniques. From electronic health record systems that streamline data management to telemedicine platforms to expand access to care, the integration of technology improves efficiency, accuracy, and patient satisfaction. Achieving healthcare quality also demands more research into effective management strategies that combine technological innovations with patient-centric care models. Improving Healthcare Quality and Patient Engagement: Management and Technology Insights explores key insights into the convergence of healthcare management and technology. It outlines the integration of healthcare quality and patient care for improved patient outcomes and reshaped healthcare services. This book covers topics such as digital technology, sustainable development, and geriatric care, and is a useful resource for medical workers, healthcare professionals, business owners, sociologists, computer engineers, data scientists, researchers, and academicians.
  large language models for healthcare: Biomedical Natural Language Processing Kevin Bretonnel Cohen, Dina Demner-Fushman, 2014-02-15 Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the perspective of both biological science and clinical medicine. The intended audience is readers who already have a background in natural language processing, but a clear introduction makes it accessible to readers from the fields of bioinformatics and computational biology, as well. The book is suitable as a reference, as well as a text for advanced courses in biomedical natural language processing and text mining.
  large language models for healthcare: LLMs and Generative AI for Healthcare Kerrie Holley, Manish Mathur, 2024-08-20 Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley, former Google healthcare professionals, guide you through the transformative potential of large language models (LLMs) and generative AI in healthcare. From personalized patient care and clinical decision support to drug discovery and public health applications, this comprehensive exploration covers real-world uses and future possibilities of LLMs and generative AI in healthcare. With this book, you will: Understand the promise and challenges of LLMs in healthcare Learn the inner workings of LLMs and generative AI Explore automation of healthcare use cases for improved operations and patient care using LLMs Dive into patient experiences and clinical decision-making using generative AI Review future applications in pharmaceutical R&D, public health, and genomics Understand ethical considerations and responsible development of LLMs in healthcare The authors illustrate generative's impact on drug development, presenting real-world examples of its ability to accelerate processes and improve outcomes across the pharmaceutical industry.--Harsh Pandey, VP, Data Analytics & Business Insights, Medidata-Dassault Kerrie Holley is a retired Google tech executive, IBM Fellow, and VP/CTO at Cisco. Holley's extensive experience includes serving as the first Technology Fellow at United Health Group (UHG), Optum, where he focused on advancing and applying AI, deep learning, and natural language processing in healthcare. Manish Mathur brings over two decades of expertise at the crossroads of healthcare and technology. A former executive at Google and Johnson & Johnson, he now serves as an independent consultant and advisor. He guides payers, providers, and life sciences companies in crafting cutting-edge healthcare solutions.
  large language models for healthcare: LLM Medical Anand Vemula, 2024-07-20 LLM Medical: Revolutionizing Healthcare with Large Language Models explores the transformative impact of advanced AI on the medical field. This book delves into the fundamentals of Large Language Models (LLMs) and their applications in healthcare, from enhancing clinical decision support and streamlining medical documentation to improving patient interaction through virtual assistants. It examines the role of LLMs in personalized medicine and predictive analytics, offering insights into their potential to revolutionize patient care and medical research. Real-world case studies highlight successful implementations, while discussions on regulatory, ethical considerations, and technical integration provide a comprehensive understanding of the challenges and opportunities. With a forward-looking perspective on emerging technologies and AI-driven innovations, this book is an essential guide for healthcare professionals, AI enthusiasts, and anyone interested in the future of medicine.
  large language models for healthcare: AI-First Healthcare Kerrie L. Holley, Siupo Becker M.D., 2021-04-19 AI is poised to transform every aspect of healthcare, including the way we manage personal health, from customer experience and clinical care to healthcare cost reductions. This practical book is one of the first to describe present and future use cases where AI can help solve pernicious healthcare problems. Kerrie Holley and Siupo Becker provide guidance to help informatics and healthcare leadership create AI strategy and implementation plans for healthcare. With this book, business stakeholders and practitioners will be able to build knowledge, a roadmap, and the confidence to support AIin their organizations—without getting into the weeds of algorithms or open source frameworks. Cowritten by an AI technologist and a medical doctor who leverages AI to solve healthcare’s most difficult challenges, this book covers: The myths and realities of AI, now and in the future Human-centered AI: what it is and how to make it possible Using various AI technologies to go beyond precision medicine How to deliver patient care using the IoT and ambient computing with AI How AI can help reduce waste in healthcare AI strategy and how to identify high-priority AI application
  large language models for healthcare: Foundation Models for General Medical AI Zhongying Deng,
  large language models for healthcare: Ethics and governance of artificial intelligence for health: large multi-modal models. WHO guidance World Health Organization, 2024-01-18 Artificial Intelligence (AI) refers to the capability of algorithms integrated into systems and tools to learn from data so that they can perform automated tasks without explicit programming of every step by a human. Generative AI is a category of AI techniques in which algorithms are trained on data sets that can be used to generate new content, such as text, images or video. This guidance addresses one type of generative AI, large multi-modal models (LMMs), which can accept one or more type of data input and generate diverse outputs that are not limited to the type of data fed into the algorithm. It has been predicted that LMMs will have wide use and application in health care, scientific research, public health and drug development. LMMs are also known as “general-purpose foundation models”, although it is not yet proven whether LMMs can accomplish a wide range of tasks and purposes.
  large language models for healthcare: Artificial Intelligence in Medical Imaging Erik R. Ranschaert, Sergey Morozov, Paul R. Algra, 2019-01-29 This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
  large language models for healthcare: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
  large language models for healthcare: Application of Large Language Models (LLMs) for Software Vulnerability Detection Omar, Marwan, Zangana, Hewa Majeed, 2024-11-01 Large Language Models (LLMs) are redefining the landscape of cybersecurity, offering innovative methods for detecting software vulnerabilities. By applying advanced AI techniques to identify and predict weaknesses in software code, including zero-day exploits and complex malware, LLMs provide a proactive approach to securing digital environments. This integration of AI and cybersecurity presents new possibilities for enhancing software security measures. Application of Large Language Models (LLMs) for Software Vulnerability Detection offers a comprehensive exploration of this groundbreaking field. These chapters are designed to bridge the gap between AI research and practical application in cybersecurity, in order to provide valuable insights for researchers, AI specialists, software developers, and industry professionals. Through real-world examples and actionable strategies, the publication will drive innovation in vulnerability detection and set new standards for leveraging AI in cybersecurity.
  large language models for healthcare: Trustworthy Artificial Intelligence for Healthcare Hao Chen,
  large language models for healthcare: Intelligence-Based Medicine Anthony C. Chang, 2020-06-27 Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare
  large language models for healthcare: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Marius George Linguraru,
  large language models for healthcare: Precision Medicine and Artificial Intelligence Michael Mahler, 2021-03-12 Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine
  large language models for healthcare: Next Generation AI Language Models in Research Kashif Naseer Qureshi, Gwanggil Jeon, 2024-11-13 In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.
  large language models for healthcare: Machine Learning for Multimodal Healthcare Data Andreas K. Maier, Julia A. Schnabel, Pallavi Tiwari, Oliver Stegle, 2023-11-25 This book constitutes the proceedings of the First International Workshop on Machine Learning for Multimodal Healthcare Date, ML4MHD 2023, held in Honolulu, Hawaii, USA, in July 2023. The 18 full papers presented were carefully reviewed and selected from 30 submissions. The workshop's primary objective was to bring together experts from diverse fields such as medicine, pathology, biology, and machine learning. With the aim to present novel methods and solutions that address healthcare challenges, especially those that arise from the complexity and heterogeneity of patient data.
  large language models for healthcare: Natural Language Processing in Biomedicine Hua Xu,
  large language models for healthcare: LLMs Ronald Legarski, 2024-09-01 LLMs: From Origin to Present and Future Applications by Ronald Legarski is an authoritative exploration of Large Language Models (LLMs) and their profound impact on artificial intelligence, machine learning, and various industries. This comprehensive guide traces the evolution of LLMs from their early beginnings to their current applications, and looks ahead to their future potential across diverse fields. Drawing on extensive research and industry expertise, Ronald Legarski provides readers with a detailed understanding of how LLMs have developed, the technologies that power them, and the transformative possibilities they offer. This book is an invaluable resource for AI professionals, researchers, and enthusiasts who want to grasp the intricacies of LLMs and their applications in the modern world. Key topics include: The Origins of LLMs: A historical perspective on the development of natural language processing and the key milestones that led to the creation of LLMs. Technological Foundations: An in-depth look at the architecture, data processing, and training techniques that underpin LLMs, including transformer models, tokenization, and attention mechanisms. Current Applications: Exploration of how LLMs are being used today in industries such as healthcare, legal services, education, content creation, and more. Ethical Considerations: A discussion on the ethical challenges and societal impacts of deploying LLMs, including bias, fairness, and the need for responsible AI governance. Future Directions: Insights into the future of LLMs, including their role in emerging technologies, interdisciplinary research, and the potential for creating more advanced AI systems. With clear explanations, practical examples, and forward-thinking perspectives, LLMs: From Origin to Present and Future Applications equips readers with the knowledge to navigate the rapidly evolving field of AI. Whether you are a seasoned AI professional, a researcher in the field, or someone with an interest in the future of technology, this book offers a thorough exploration of LLMs and their significance in the digital age. Discover how LLMs are reshaping industries, driving innovation, and what the future holds for these powerful AI models.
  large language models for healthcare: Mastering AI Jeremy Kahn, 2024-08-01 An urgent book on generative artificial intelligence exploring the risk and benefits looming in this seminal moment 'Easily the best exploration to date on the perils and promise of AI. —ASHLEE VANCE author of When the Heavens Went on Sale 'Mastering AI is a must-read. It's hard to put down'. —BETHANY McLEAN, coauthor of The Smartest Guys in the Room and The Big Fail ' A timely and urgent exploration of AI's dizzying acceleration' —BRAD STONE, author of The Everything Store The debut of ChatGPT on November 30th was a watershed moment in the history of technology. We stand on the threshold of a new age — one where content of all kinds, even software itself, will be conjured, seemingly from thin air, with simple conversation. In a culture fraught with misinformation, Mastering AI pierces through the thicket of exaggerated claims, explaining how we arrived at this moment and mapping the likely long-term impacts on business, economics, culture and society this potent technology will have. This book will serve as a guide to those dangers — as well as highlighting the technology's transformative potential — and will pinpoint concrete steps that should be taken to regulate generative AI.
  large language models for healthcare: Maximizing Productivity with ChatGPT Jason Brownlee, Adrian Tam, Matthew Mayo, Abid Ali Awan, Kanwal Mehreen, 2023-07-25 ChatGPT is one of the leading models in the AI language model arena and is widely used in various fields. With ChatGPT, you can effortlessly harness the power of AI to improve your efficiency with just a few well-crafted prompts. Many productivity-boosting tasks are facilitated by ChatGPT, so understanding how to interact with it paves the way for you to leverage the power of advanced AI. This ebook is written in the engaging and approachable style that you’re familiar with from the Machine Learning Mastery series. Discover exactly how to get started and apply ChatGPT to your own productivity, learning, or creativity projects.
  large language models for healthcare: AI for Health Equity and Fairness Arash Shaban-Nejad,
  large language models for healthcare: Metaverse Applications for Intelligent Healthcare Gaur, Loveleen, Jhanjhi, Noor Zaman, 2023-11-24 Metaverse Applications for Intelligent Healthcare explores the exciting intersection of artificial intelligence (AI) and the metaverse in the field of healthcare. The use of AI technology in healthcare has already shown great potential in assisting with diagnosis, treatment, and patient care. The metaverse, with its immersive virtual environments, has the potential to revolutionize healthcare by making it more accessible, efficient, and personalized. This book introduces various applications of the metaverse in healthcare, including virtual consultations, remote patient monitoring, and virtual rehabilitation. The book discusses how the metaverse can be used to provide immersive experiences that empower patients and providers, while also offering unique learning opportunities. The book is ideal for researchers, practitioners, healthcare professionals, scholars, and students who are interested in exploring the cutting-edge technology of AI and the metaverse in healthcare. It offers insights into the future of healthcare, and how these technologies can be used to provide better care to patients. By combining the latest research in AI and the metaverse, this book provides a comprehensive overview of the potential applications of these technologies in healthcare.
  large language models for healthcare: Distributed, Ambient and Pervasive Interactions Norbert A. Streitz,
  large language models for healthcare: 11th European Conference on Social Media Dr Panagiotis Fotaris, 2024-05-30 These proceedings represent the work of contributors to the 11th European Conference on Social Media (ECSM 2024), hosted by the University of Brighton, UK on 30-31 May 2024. The Conference and Programme Chair is Dr Panagiotis Fotaris from the University of Brighton. ECSM is now a well-established event on the academic research calendar and now in its 11th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research.
  large language models for healthcare: Data Science and Information Security Hai Jin,
  large language models for healthcare: Biocomputing 2024 - Proceedings Of The Pacific Symposium Russ B Altman, Lawrence Hunter, Marylyn D Ritchie, Tiffany A Murray, Teri E Klein, 2023-12-18 The Pacific Symposium on Biocomputing (PSB) 2024 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2024 will be held on January 3 - 7, 2024 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2024 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
  large language models for healthcare: Research Handbook on Health, AI and the Law Barry Solaiman, I. Glenn Cohen, 2024-07-05 This is an open access title available under the terms of a CC BY-NC-ND 4.0 License. It is free to read, download and share on Elgaronline, thanks to generous funding support from Hamad Bin Khalifa University (HBKU). The Research Handbook on Health, AI and the Law explores the use of AI in healthcare, identifying the important laws and ethical issues that arise from its use. Adopting an international approach, it analyses the varying responses of multiple jurisdictions to the use of AI and examines the influence of major religious and secular ethical traditions.
  large language models for healthcare: Health Information Processing. Evaluation Track Papers Hua Xu,
  large language models for healthcare: Computational Science – ICCS 2024 Leonardo Franco,
  large language models for healthcare: Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1 Kohei Arai, 2023-11-01 This book is a collection of thoroughly well-researched studies presented at the Eighth Future Technologies Conference. This annual conference aims to seek submissions from the wide arena of studies like Computing, Communication, Machine Vision, Artificial Intelligence, Ambient Intelligence, Security, and e-Learning. With an impressive 490 paper submissions, FTC emerged as a hybrid event of unparalleled success, where visionary minds explored groundbreaking solutions to the most pressing challenges across diverse fields. These groundbreaking findings open a window for vital conversation on information technologies in our community especially to foster future collaboration with one another. We hope that the readers find this book interesting and inspiring and render their enthusiastic support toward it.
  large language models for healthcare: Databases Theory and Applications Zhifeng Bao, Renata Borovica-Gajic, Ruihong Qiu, Farhana Choudhury, Zhengyi Yang, 2023-12-08 This book constitutes the refereed proceedings of the 34th Australasian Database Conference on Databases Theory and Applications, ADC 2023, held in Melbourne, VIC, Australia, during November 1-3, 2023. The 26 full papers presented in this volume are carefully reviewed and selected from 41 submissions. They were organized in topical sections named: Mining Complex Types of Data, Natural Language Processing and Text Analysis, Machine Learning and Computer Vision, Database Systems and Data Storage, Data Quality and Fairness for Graphs and Graph Mining and Graph Algorithms.
  large language models for healthcare: Smart Mobile Communication & Artificial Intelligence Michael E. Auer, Thrasyvoulos Tsiatsos, 2024 Zusammenfassung: Interactive mobile technologies are today the core of many--if not all--fields of society. Not only the younger generation of students expects a mobile working and learning environment. And nearly daily new ideas, technologies, and solutions boost this trend. To discuss and assess the trends in the interactive mobile field are the aims connected with the 15th International Conference on Interactive Mobile Communication, Technologies, and Learning (IMCL2023), which was held 9-10 November 2023. Since its beginning in 2006, this conference is devoted to new approaches in interactive mobile technologies with a focus on learning. Nowadays, the IMCL conferences are a forum of the exchange of new research results and relevant trends as well as the exchange of experiences and examples of good practice. Interested readership includes policy makers, academics, educators, researchers in pedagogy and learning theory, schoolteachers, learning Industry, further education lecturers, etc
  large language models for healthcare: Advances in Digital Health and Medical Bioengineering Hariton-Nicolae Costin,
  large language models for healthcare: Artificial Intelligence in Healthcare Xianghua Xie,
  large language models for healthcare: Multisector Insights in Healthcare, Social Sciences, Society, and Technology Burrell, Darrell Norman, 2024-02-27 Due to a variety of global challenges in recent times, the dissolution of traditional boundaries between academic disciplines has given rise to a pressing need for innovative problem-solving. Complex issues affect our societies, spanning healthcare, social sciences, organizational behavior, and technology. This shifting landscape necessitates a comprehensive exploration into the interconnections between these diverse fields. The book, Multisector Insights in Healthcare, Social Sciences, Society, and Technology, is an innovative guide that seeks to examine the relationships between various fields of knowledge. It celebrates the transformative impact of applied research and interdisciplinary collaboration as the driving force behind overcoming the most significant challenges of our time. As the boundaries between disciplines blur, the book takes readers on a journey through multifaceted issues at the intersection of healthcare, social sciences, organizational behavior, and technology. Chapters within this book unravel the complexities of healthcare ethics, global health initiatives, organizational dynamics, and technological advancements. Through literature reviews, qualitative and quantitative studies, and real-world case analyses, the compendium not only identifies the problems but also offers concrete, evidence-backed solutions. This interdisciplinary approach underscores the need to address the pressing challenges of our time, emphasizing the need for collaborative strategies to drive positive change.
  large language models for healthcare: Digital Health Dipu Patel, 2024-10-21 Digital Health: Telemedicine and Beyond describes practical ways to use digital health tools in clinical practice. With a strong focus on case studies and patient outcomes, this title provides an overview of digital medicine, terms, concepts, and applications for the multidisciplinary clinical practitioner. Chapters provide a concise, yet comprehensive understanding of digital health, including telemedicine, mHealth, EHRs, and the benefits and challenges of each. The book gives insights on risks and benefits associated with storing and transmitting patient information via digital tools and educates clinicians in the correct questions to ask for advocacy regarding state laws, scope of practice, and medicolegal implications. It also addresses the ethical and social challenges that digital health raises, how to engage patients to improve shared decision-making models and how digital health tools can be integrated into clinical practice. This book is a valuable resource for clinicians and medical educators of all health professions, including physicians, physician associates, nurses, pharmacists, physical therapists, occupational therapists, speech therapists, students, and all those who wish to broaden their knowledge in the allied field. - Provides a clinical perspective on digital health - Written by clinicians for clinicians with the patient in mind - Describes practical ways to use digital health tools in clinical practice - Includes case studies to incorporate workflows into practice to improve patient outcomes
  large language models for healthcare: Electronic Government and the Information Systems Perspective Andrea Kő, Enrico Francesconi, Gabriele Kotsis, A Min Tjoa, Ismail Khalil, 2022-07-28 This volume LNCS 12429 constitutes the papers of the 11th International Conference on Electronic Government and the Information Systems Perspective, EGOVIS 20221, held in Vienna, Austria, in August 2022. The 11 full papers presented were carefully reviewed and selected from 16 submissions and focus on information systems and ICT aspects of e-government. The papers are organized in 3 topical sections: e-government theoretical background; semantic technologies and legal issues;; artificial intelligence and machine learning in e-government context.
  large language models for healthcare: A Glimpse at Medicine in the Future Mandana Hasanzad,
Large Language Models in Healthcare: A Comprehensive …
24 Apr 2024 · In this pa-per, we comprehensively benchmark diverse LLMs in healthcare, to clearly understand their strengths and weaknesses. Our benchmark con-tains seven tasks and thirteen datasets across medical language generation, understanding, and reasoning.

Large language models in medical and healthcare fields
Large language models (LLMs) are increasingly recognized for their advanced language capabilities, ofering significant assistance in diverse areas like medical communication, patient …

Abstract Large Language Models in the Clinic: A Comprehensive
Large language models (LLMs), such as ChatGPT (OpenAI,2023b), are increasingly being recog-nized for their potential in healthcare to aid clinical decision-making. Recently, many efforts …

The future landscape of large language models in medicine
Here, we provide an overview of how LLMs could impact patient care, medical research and medical education.

Large language models in medicine - Nature
Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings.

A Systematic Review of ChatGPT and Other Conversational …
26 Apr 2024 · Objective: This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in …

Generative AI and large language models in health care ... - Nature
Noting those limitations, Wornow et al. propose an improved framework to evaluate generative AI models for healthcare settings. They elaborate upon six criteria: predictive performance, data...

Large language models in health care: Development, …
In this review, we provide an overview of the development of LLMs designed for biomedical or clinical use. We subsequently explore the potential and trialed applications of LLMs in clinical …

Large language models in medicine: the potentials and pitfalls
Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships …

Large language models for medicine: a survey - Springer
In this paper, we review LLM developments, focusing on the requirements and applications of medical LLMs. We provide a concise overview of existing models, aiming to explore advanced …

LLMs-Healthcare : Current Applications and Challenges of Large …
The data suggests the growing viability of large language models like ChatGPT in enhancing radiologic decision-making processes, with potential benefits for clinical workflows and more …

Large Language Models in Healthcare Current Development and …
LLMs can assist healthcare professionals by providing accurate information, supporting diagnostic processes, and enhancing medical education. However, the implementation of LLMs in clinical …

Systematic Review of Large Language Models for Patient Care: …
4 Mar 2024 · The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and …

A study of generative large language model for medical ... - Nature
Fig. 1 Develop a clinical generative large language model, GatorTronGPT, for biomedical natural language processing, clinical text generation, and healthcare text evaluation. a Train …

Large language models in healthcare: from a systematic review …
Large language models (LLMs) have the intrinsic potential to acquire medical knowledge. Several studies assessing LLMs on medical examinations have been published. However, there is no …

Better to Ask in English: Cross-Lingual Evaluation of Large …
13 May 2024 · Large language models (LLMs) are transforming the ways the gen-eral public accesses and consumes information. Their influence is particularly pronounced in pivotal …

Large language models in healthcare information research: …
23 Oct 2024 · LLMs have fundamentally changed how machine learning is used across domains. Unlike previous generation systems that required careful data curation for specific tasks before …

Large Language Models and Healthcare Alliance: Potential and …
Large language models (LLMS) emerge as the most promising Natural Language Processing approach for clinical practice acceleration (i.e., diagnosis, prevention and treatment …

A Systematic Review of Testing and Evaluation of Healthcare ...
15 Apr 2024 · To draw concrete insights on their performance, evaluations need to use real patient care data across a broad range of healthcare and NLP/NLU tasks and medical …

Large language models could change the future of behavioral …
Large language models (LLMs), built on artificial intelligence (AI) such as Open AI’s GPT-4 (which power ChatGPT) and Google’s Gemini – are breakthrough technologies that can read, …

Large language models in medicine - Nature
Large language models in medicine Arun James Thirunavukarasu 1,2 , Darren Shu Jeng Ting 3,4,5 , Kabilan Elangovan 6 , Laura Gutierrez 6 , Ting Fang Tan 6,7 &

Leveraging Large Language Models for Patient Engagement: The …
Large language models (LLMs), deep learning models trained on vast amounts of text data, have emerged as a particularly promising tool for unlocking insights and automating tasks across various healthcare domains. With their ability to understand, generate, and reason with natural language, LLMs are enabling new

A Systematic Review of Testing and Evaluation of Healthcare ...
15 Apr 2024 · Question: How are healthcare applications of large language models (LLMs) currently evaluated? Findings: Studies rarely used real patient care data for LLM evaluation. Administrative tasks such as generating provider billing codes and writing prescriptions were understudied. Natural Language Processing (NLP)/Natural Language Understanding

Future of Large Language Models and Digital Twins in Precision ...
overview of the recent advances, applications, and challenges of digital twins and large language models in precision healthcare. It also proposes a state-of-the-art technology that combines a ...

The Arrival of Artificial Intelligence Large Language Models and …
The Arrival of Artificial Intelligence Large Language Models and Vision-Language Models: A Potential to Possible Change in the Paradigm of Healthcare Delivery in Dermatology Aditya K. Gupta1,2, Mesbah Talukder1,3, Tong Wang1, Roxana Daneshjou4,5 and Vincent Piguet2,6

Navigating the future: The impact of generative AI and large language ...
5 Apr 2024 · generative AI and large language models in healthcare The healthcare industry is on the verge of a technological transformation, driven by breakthroughs in generative Artificial Intelligence (AI) and Large Language Models (LLMs). These developments are poised to change patient care, research, and administrative effectiveness.

Prepare for truly useful large language models - Nature
Large language models and large vision models will have all sorts of profound conse-quences. It is a rather safe bet that they will change many industries over time, especially

Understanding The Concerns and Choices of The Public When Using Large …
Additional Key Words and Phrases: Large Language Models, Healthcare, Public Perception, Ethics 1 INTRODUCTION Large language models (LLMs) have been a topic of great interest in recent years, especially in the field of natural language processing (NLP). LLMs are a type of AI model designed to generate human-like text by analyzing vast amounts ...

LLMs-Healthcare : Current Applications and Challenges of Large Language ...
Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions

Guiding IoT-Based Healthcare Alert Systems with Large Language Models
Index Terms—Healthcare alert systems, generative AI (GAI), large language models (LLMs), mixture of experts (MoE). I. INTRODUCTION The penetration of the Internet of Things (IoT), big data, artificial intelligence (AI), cloud computing, and mobile appli-cations into our society is revolutionizing healthcare. At the

Large Language Model Integrated Healthcare Cyber-Physical …
automate complex processes. Large Language Model (LLM) has the potential to transform HCPS by offering cutting-edge AI technologies. These cutting-edge LLM models, like the Generative Pre-trained Transformer (GPT) models from Ope-nAI, can read, understand, and produce human-like content at remarkable lengths [2]. A vast family of LLM models,

Benchmarking medical large language models - Nature
Title: Benchmarking medical large language models Author: Sadra Bakhshandeh Subject: Nature Reviews Bioengineering, doi:10.1038/s44222-023-00097-7

Are Large Language Models Ready for Healthcare? A …
Large Language Models in Healthcare: A Comparative Study 3. Enriching Context: The model then “Answer these questions using basic medical knowledge and use the insights to evaluate their relationship”. This prompt instructs the model to deepen its understanding. 4. Task-Specific Strategy:Lastly, the model follows the prompt to “Categorize the

Large Language Models in Dutch Healthcare - Universiteit Utrecht
2.3 large language models in healthcare 13 2.4 a brief overview of ai in dutch healthcare 14 3 theory 16 3.1 innovation systems & tis 16 3.2 an integrated framework 18 4 methodology 23 4.1 research approach 23 4.2 tis analysis steps 23 4.3 data-collection 24 4.4 analysis 31 ...

Large language models in medicine: the potentials and pitfalls
Large language models in medicine: the potentials and pitfalls Jesutofunmi A. Omiye 1*, Haiwen Gui*, Shawheen J. Rezaei1, ... healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine. 1. Introduction: Large Language models (LLMs) have become increasingly mainstream since the launch of OpenAI's (San ...

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large …
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. The great promise of LLMs as general task solvers motivated people to ... in specific domains such as healthcare, finance, and education. However, directly ...

A Survey on Large Language Models for Critical Societal Domains ...
A Survey on Large Language Models for Critical Societal Domains: Finance,Healthcare,andLaw ZhiyuZoeyChen*,The University of Texas at Dallas zhiyu.chen2@utdallas.edu JingMa*,Case Western Reserve University jing.ma5@case.edu XinluZhang*,University of California, Santa Barbara xinluzhang@ucsb.edu NanHao*,Stony Brook University hao.nan@stonybrook.edu

arXiv:2310.07282v2 [cs.AI] 12 Oct 2023
12 Oct 2023 · 3 Language Models and Healthcare Large Language Models (LLMs) are one of the most exciting areas in Artificial Intelligence (AI) research that have the ability to process and generate human-like text for various healthcare applications. The more data we train, the more predictions will be more accurate.

Large Language Models Meet NLP: A Survey - arXiv.org
NLP Large Language Models Meet NLP: A Survey Libo Qin♣ Qiguang Chen♠ Xiachong Feng♢ Yang Wu♠ Yongheng Zhang♣ Yinghui Li♮ Min Li♣ Wanxiang Che♠ Philip S. Yu♡ ♣ Central South University ♠ Harbin Institute of Technology ♢ University of Hong Kong ♮ Tsinghua University ♡ University of Illinons at Chicago lbqin@csu.edu.cn, {qgchen,car}@ir.hit.edu.cn

The Future of Medicine: Large Language Models Redefining Healthcare …
The Future of Medicine: Large Language Models Redefining Healthcare Dynamics 1st Ahshanul Haque Dept of Computer Science New Mexico Tech Socorro, NM, USA ahshanul.haque@student.nmt.edu

LLMs-Healthcare : Current Applications and Challenges of Large Language ...
Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions

A Survey of Large Language Models in Medicine: Progress, …
The recently emerged general large language models (LLMs)1,2, such as PaLM3, LLaMA4,5, GPT-series6,7, and ChatGLM8,9, ... assesses the trustworthiness of medical LLMs to ensure their responsible and effective utilization in healthcare. For the last question, we propose future research directions to advance the medical LLMs field. ...

Schema Matching with Large Language Models: an Experimental …
17 Jul 2024 · In the healthcare data integration context, we found that, despite its restrictions, we often have schema docu-mentation in the form of data dictionaries available, as well as natural-language descriptions of some schema elements. In particular, target schemas are often common data models: data schemas designed by community con-

The Utility of ChatGPT as an Example of Large Language Models in
21 Feb 2023 · The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations . Author . Malik Sallam . 1,2, * Affiliations . 1. Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman ...

A Study of Generative Large Language Model for Medical …
24 May 2023 · There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters.

Large language models will not replace healthcare professionals ...
31 May 2023 · Large language models will not replace healthcare professionals: curbing popular fears and hype Arun James Thirunavukarasu Corpus Christi College, University of Cambridge, Cambridge, CB2 1RH, UK Corresponding author: Arun James Thirunavukarasu. Email: ajt205@cantab.ac.uk Following the release of ChatGPT, large language

A Survey on Large Language Models for Critical Societal Domains ...
A Survey on Large Language Models for Critical Societal Domains: Finance,Healthcare,andLaw ZhiyuZoeyChen*,The University of Texas at Dallas zhiyu.chen2@utdallas.edu JingMa*,Case Western Reserve University jing.ma5@case.edu XinluZhang*,University of California, Santa Barbara xinluzhang@ucsb.edu NanHao*,Stony Brook University hao.nan@stonybrook.edu …

Large Language Models for Mental Health Applications: A
Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate.

Large language models and multimodal foundation models for …
11. Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). 12. Singhal, K. et al. Towards expert-level medical question answering with large language models.

A large language model for electronic health records - Nature
A large language model for electronic health records Xi Yang1,2, Aokun Chen1,2, Nima PourNejatian3, ... healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc ...

The shaky foundations of large language models and foundation models …
The shaky foundations of large language models and foundation models for electronic health records ... foundation models that is more closely grounded to metrics that matter in healthcare. npj ...

Systematic Review of Large Language Models for Patient Care: …
4 Mar 2024 · prognostic models. 11,12 In contrast to applications primarily aimed at healthcare professionals, LLMs could also be used to promote patient education and empowerment by providing answers to medical questions and translating complex medical information into more accessible language.4,13 Thereby, LLMs may promote personalized medicine and broaden

A scoping review of using Large Language Models (LLMs) to …
7 May 2024 · NLP models trained on data from one healthcare institution often struggle to generalize effectively to data from other institutions. As a result, training NLP models can require a substantial amount of human annotation29. ... Large Language Models (LLMs) have recently emerged as novel technologies for language processing. ...

LLM in mental health care-JAMIA - arXiv.org
Large Language Models (LLMs), one of the most recent advances in NLP, have further expanded the potential for innovative mental health care.16,17 As a type of Artificial Intelligence (AI) that understands and generates human-like and fluent texts, LLMs offer many promising applications for mental health. For example, their ability to efficiently

Large Language Models for Time Series: A Survey - IJCAI
Figure 1: Large language models have recently been applied for var-ious time series tasks in diverse application domains. In recent years, Large Language Models (LLMs) have gained substantial attention particularly in the elds of Natu-ral Language Processing (NLP) and Computer Vision (CV). Prominent models such as GPT-4 have transformed the land-

PROCEEDINGS OF THE IEEE 1 A Survey of Large Language Models …
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics Kai He, Member, IEEE, Rui Mao Member, IEEE, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng∗, Senior Member, IEEE Erik Cambria, Fellow, IEEE Abstract—The utilization of large language models (LLMs)

CollectiveSFT: Scaling Large Language Models for Chinese …
datasets. They use advanced language models like GPT-4 to reconstruct these dialogues into question-answer pairs for model training. This method aims to improve the models’ understand-ing of medical consultations and enhance their ability to provideaccurate and relevant responses. However, these models also come with notable limitations.

Are Large Language Models Ready for Healthcare? A …
Large Language Models in Healthcare: A Comparative Study 3. Enriching Context: The model then “Answer these questions using basic medical knowledge and use the insights to evaluate their relationship”. This prompt instructs the model to deepen its understanding. 4. Task-Specific Strategy:Lastly, the model follows the prompt to “Categorize the

Mental-LLM: Leveraging Large Language Models for Mental …
Additional Key Words and Phrases: Mental Health, Large Language Model, Instruction Finetuning ACM Reference Format: Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel, Hong Yu, James Hendler, Marzyeh Ghassemi, Anind K. Dey, and Dakuo Wang. 2024. Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data.

A Comprehensive Overview of Large Language Models - arXiv.org
A Comprehensive Overview of Large Language Models Humza Naveeda, Asad Ullah Khanb, ∗, Shi Qiuc,, Muhammad Saqibd,e,∗, Saeed Anwarf,g, Muhammad Usmanf,g, Naveed Akhtarh,j, Nick Barnesi, Ajmal Mianj aThe University of Sydney, Sydney, Australia bUniversity of Engineering and Technology (UET), Lahore, Pakistan cThe Chinese University of Hong Kong …

Reporting Guidelines for Artificial Intelligence Studies in Healthcare ...
on large multimodal models, more frequently referred to as large language models (LLMs), and offers specific recommendations for reporting on LLM studies. Release of TRIPOD+AI and CLAIM 2024 Update The TRIPOD+AI and CLAIM 2024 have recently been published [2,6]. Both guidelines focus on evaluating model development and performance.

Large Language Models and Generative AI’s Expanding Role in Healthcare
Large language models and generative artificial intelligence (GAI) have recently demonstrated significant promise for revolutionizing a range of industries, including healthcare.

arXiv:2307.13693v2 [cs.CL] 27 Jul 2023
RadLLM: A Comprehensive Healthcare Benchmark of Large Language Models for Radiology Zhengliang Liu ∗1, Tianyang Zhong 2, Yiwei Li , Yutong Zhang †8, Yi Pan †6, Zihao Zhao †11, Peixin Dong †2, Chao Cao †4, Yuxiao Liu †11,20, Peng Shu †1, Yaonai Wei †2, Zihao Wu1, Chong Ma 2, Jiaqi Wang10, Sheng Wang17, Mengyue Zhou , Zuowei Jiang2, Chunlin Li2, ...

Aloe: A Family of Fine-tuned Open Healthcare LLMs - arXiv.org
Abstract. As the capabilities of Large Language Models (LLMs) in healthcare and medicine continue to advance, there is a growing need for competitive open-source models that can safeguard public interest. With the increasing availability of highly competitive open base models, the impact of continued pre-training is increasingly un-certain.

Large Language Models for Medicine: A Survey - arXiv.org
LargeLanguageModelsforMedicine:ASurvey YanxinZhenga,WenshengGana,∗,ZefengChena,ZhenlianQib,QianLiangc andPhilipS.Yud aCollege of Cyber Security, Jinan University, Guangzhou 510632, China bSchool of Information Engineering, Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, China cShenzhen …

The Impact of Large Language Models in Finance: Towards …
text outputs [14]. In general, language models involve modelling the probability of word sequences to predict the likelihood of future word sequences. The capability of processing and utilising vast amounts of existing linguistic data differentiates large language models (LLMs) from the traditional language models [13].

Evaluating large language models in medical applications: a survey
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance

The Future of Intelligent Healthcare: A Systematic Analysis and ...
Large Language Models (LLMs) for Healthcare The versatility of generative AI is apparent in its ability to be trained on an array of data types from textual (i.e., EHRs) [33] and visual content (i ...

The Utility of ChatGPT as an Example of Large Language Models in
19 Feb 2023 · The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations . Author . Malik Sallam . 1,2, * Affiliations . 1. Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman ...

Are Large Language Models Ready for Healthcare? Comparative …
• Large Language Models (LLMs) have substantial untapped potential for healthcare revolution - a topic yet to be comprehensively evaluated and fully appreciated. • There is a need to explore the efficacy of diverse prompting techniques, such as the proposed self-questioning prompting, in ...