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AI in Medical Writing: Revolutionizing Healthcare Communication
Author: Dr. Anya Sharma, PhD, Associate Professor of Biomedical Informatics, University of California, San Francisco. Dr. Sharma has over 15 years of experience in applying AI to healthcare data analysis and medical communication.
Publisher: BioMed Central – A leading open-access publisher of peer-reviewed biomedical journals, known for its rigorous editorial process and commitment to disseminating high-quality research.
Editor: Dr. Emily Carter, MD, PhD, Medical Editor with over 20 years of experience editing medical publications for prestigious journals.
Keywords: AI in medical writing, artificial intelligence in medical writing, AI medical writing tools, AI for medical writing, AI-powered medical writing, medical writing AI, future of medical writing, benefits of AI in medical writing, challenges of AI in medical writing, ethical considerations of AI in medical writing.
Abstract: The integration of artificial intelligence (AI) into medical writing is rapidly transforming the landscape of healthcare communication. This article explores the multifaceted impact of AI in medical writing, analyzing its significant contributions to efficiency, accuracy, and accessibility. We will delve into the various applications of AI, including automated report generation, literature review assistance, grammar and style checking, and the creation of personalized patient information. However, we also address the crucial ethical considerations and potential challenges associated with relying on AI in a field requiring precision and human judgment. The ultimate goal is to provide a comprehensive overview of the current state and future prospects of AI in medical writing, emphasizing its potential to improve healthcare communication and patient outcomes while acknowledging the limitations and responsible implementation strategies.
1. Introduction: The Rise of AI in Medical Writing
The medical writing field, demanding accuracy, clarity, and adherence to strict regulatory guidelines, is experiencing a significant paradigm shift with the integration of AI. The sheer volume of medical data generated daily, coupled with the increasing need for efficient communication, has created an environment ripe for AI-powered solutions. "AI in medical writing" is no longer a futuristic concept; it's a rapidly evolving reality, offering significant improvements in speed, consistency, and even quality of medical publications, regulatory documents, and patient-facing materials.
2. Applications of AI in Medical Writing
AI's applications in medical writing are diverse and continuously expanding. Some key areas include:
Automated Report Generation: AI algorithms can analyze clinical trial data, automatically generating initial drafts of reports, significantly reducing the time and effort required by human writers. This is particularly useful for repetitive tasks, freeing medical writers to focus on higher-level tasks requiring critical thinking and interpretation.
Literature Review Assistance: AI can sift through vast databases of medical literature, identifying relevant articles and summarizing key findings. This can drastically shorten the time required for literature reviews, allowing medical writers to focus on synthesis and analysis.
Grammar and Style Checking: AI-powered tools offer advanced grammar and style checking capabilities, ensuring consistency in writing style and adherence to specific publication guidelines. This goes beyond basic grammar checks, incorporating medical style guides and ensuring consistency in terminology.
Personalized Patient Information: AI can be used to create personalized patient information materials tailored to individual needs and understanding levels. This improves patient engagement and comprehension, leading to better health outcomes.
Regulatory Document Preparation: AI can assist in creating regulatory submissions, ensuring compliance with complex guidelines and regulations. This reduces the risk of errors and delays in the drug development process.
3. Benefits of AI in Medical Writing
The integration of AI offers several key benefits to the field:
Increased Efficiency: AI automates repetitive tasks, freeing up medical writers to focus on more complex and creative aspects of their work.
Improved Accuracy: AI reduces the risk of human error, improving the accuracy and consistency of medical documents.
Enhanced Accessibility: AI-powered tools can make medical information more accessible to a wider audience through personalization and translation capabilities.
Cost Reduction: By automating tasks and improving efficiency, AI can contribute to significant cost reductions in medical writing.
4. Challenges and Ethical Considerations of AI in Medical Writing
While the benefits are substantial, the integration of AI in medical writing also presents challenges:
Data Bias: AI algorithms are trained on existing data, which may contain biases. This can lead to biased outputs, requiring careful monitoring and mitigation strategies.
Lack of Transparency: Some AI algorithms operate as "black boxes," making it difficult to understand how they arrive at their conclusions. This lack of transparency can be problematic in a field demanding accountability and traceability.
Maintaining Human Oversight: It is crucial to maintain human oversight in the medical writing process, ensuring that AI-generated content is accurate, ethically sound, and appropriately contextualized.
Job Displacement Concerns: The automation of certain tasks may raise concerns about job displacement for medical writers. However, the focus should be on augmenting human capabilities rather than replacing them entirely.
Intellectual Property Rights: Clarifying intellectual property rights related to AI-generated content is critical to avoid legal disputes and ensure fair attribution.
5. The Future of AI in Medical Writing
The future of AI in medical writing is bright. We can expect to see:
More sophisticated AI models: AI algorithms will continue to improve in their ability to understand and generate complex medical texts.
Wider adoption of AI tools: More medical writing professionals will integrate AI tools into their workflows.
Increased collaboration between humans and AI: The focus will be on collaborative workflows, with AI augmenting human capabilities rather than replacing them.
New applications of AI: AI will continue to find new applications in medical writing, addressing previously unmet needs.
6. Conclusion:
AI is rapidly transforming the landscape of medical writing, offering significant benefits in terms of efficiency, accuracy, and accessibility. However, it is crucial to address the ethical considerations and potential challenges associated with its implementation. By embracing a responsible and human-centered approach, we can harness the power of AI to improve healthcare communication and ultimately contribute to better patient outcomes. The key lies in strategically integrating AI to augment, not replace, the expertise and critical thinking skills of human medical writers. The future of medical writing will undoubtedly be shaped by a powerful collaboration between human ingenuity and artificial intelligence.
FAQs:
1. Is AI replacing medical writers? No, AI is augmenting the work of medical writers, automating routine tasks and allowing them to focus on higher-level responsibilities.
2. How can I learn more about using AI tools in medical writing? Online courses, workshops, and professional development programs offer training on AI-powered medical writing tools.
3. What are the ethical implications of using AI-generated medical content? Ensuring accuracy, avoiding bias, maintaining transparency, and protecting intellectual property are key ethical considerations.
4. What are the potential legal ramifications of using AI in medical writing? Legal ramifications may arise from issues like copyright infringement, data privacy, and liability for errors.
5. How can I ensure the accuracy of AI-generated medical content? Human review and validation are crucial to ensure accuracy and prevent the propagation of misinformation.
6. What types of medical documents can benefit most from AI assistance? Regulatory submissions, clinical trial reports, and patient information materials are prime candidates.
7. What are the costs associated with implementing AI tools in medical writing? Costs vary based on the specific tools and software chosen, but they often offer significant long-term cost savings.
8. How can I evaluate the reliability and validity of different AI medical writing tools? Consider factors like accuracy, user reviews, and the reputation of the developer.
9. What is the future outlook for AI in medical writing? The future involves even more sophisticated tools that enhance collaboration between human writers and AI, leading to greater efficiency and higher-quality medical communication.
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2. "Ethical Considerations in the Use of AI for Medical Writing": This article delves into the ethical implications of using AI in medical writing, addressing issues such as bias, transparency, and accountability.
3. "Improving the Efficiency of Medical Writing with AI-Driven Automation": This article focuses on the practical applications of AI in automating medical writing tasks, enhancing efficiency and reducing workload.
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| ai in medical writing: 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 |
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| ai in medical writing: 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. |
| ai in medical writing: Oxford Handbook of Ethics of AI Markus D. Dubber, Frank Pasquale, Sunit Das, 2020-06-30 This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term A.I. is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether human or A.I. |
| ai in medical writing: Essentials of Writing Biomedical Research Papers. Second Edition Mimi Zeiger, 1999-10-21 Provides immediate help for anyone preparing a biomedical paper by givin specific advice on organizing the components of the paper, effective writing techniques, writing an effective results sections, documentation issues, sentence structure and much more. The new edition includes new examples from the current literature including many involving molecular biology, expanded exercises at the end of the book, revised explanations on linking key terms, transition clauses, uses of subheads, and emphases. If you plan to do any medical writing, read this book first and get an immediate advantage. |
| ai in medical writing: 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 |
| ai in medical writing: Practical AI for Healthcare Professionals Abhinav Suri, 2021-12-14 Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You’ll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You’ll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you’ll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once you’ve mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images. The topics covered in this book not only encompass areas of the medical field where AI is already playing a major role, but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to those problems. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients. |
| ai in medical writing: Medical Writing Neville W. Goodman, Martin B. Edwards, 2006-10-26 Effective communication is the ultimate, but often daunting, purpose of any piece of medical research. Medical Writing: A Prescription for Clarity provides practical information enabling first drafts to be turned into clear, simple, unambiguous text, without loss of individuality. Written by a medical consultant and an experienced medical editor, it is sympathetic to the problems and needs of medical writers. Like the preceding two editions, this expanded third edition deals with the basic craft of writing for publication, from spelling and grammar to choosing the best word or phrase. Whether writing a simple clinical report or thesis, wanting to supervise others, or wanting just to develop greater skill in effective writing, this book is the ideal guide and reference. Clear, simple and precise, and illustrated with apt cartoons, this is an invaluable handbook. |
| ai in medical writing: Clinical Trials Handbook Shayne Cox Gad, 2009-06-17 Best practices for conducting effective and safe clinical trials Clinical trials are arguably the most important steps in proving drug effectiveness and safety for public use. They require intensive planning and organization and involve a wide range of disciplines: data management, biostatistics, pharmacology, toxicology, modeling and simulation, regulatory monitoring, ethics, and particular issues for given disease areas. Clinical Trials Handbook provides a comprehensive and thorough reference on the basics and practices of clinical trials. With contributions from a range of international authors, the book takes the reader through each trial phase, technique, and issue. Chapters cover every key aspect of preparing and conducting clinical trials, including: Interdisciplinary topics that have to be coordinated for a successful clinical trialData management (and adverse event reporting systems) Biostatistics, pharmacology, and toxicology Modeling and simulation Regulatory monitoring and ethics Particular issues for given disease areas-cardiology, oncology, cognitive, dementia, dermatology, neuroscience, and more With unique information on such current issues as adverse event reporting (AER) systems, adaptive trial designs, and crossover trial designs, Clinical Trials Handbook will be a ready reference for pharmaceutical scientists, statisticians, researchers, and the many other professionals involved in drug development. |
| ai in medical writing: The Halo Effect Phil Rosenzweig, 2008-12-09 Why do some companies prosper while others fail? Despite great amounts of research, many of the studies that claim to pin down the secret of success are based in pseudoscience. THE HALO EFFECT is the outcome of that pseudoscience, a myth that Philip Rosenzweig masterfully debunks in THE HALO EFFECT. THE HALO EFFECT highlights the tendency of experts to point to the high financial performance of a successful company and then spread its golden glow to all of the company's attributes - clear strategy, strong values, and brilliant leadership. But in fact, as Rosenzweig clearly illustrates, the experts are not just wrong, but deluded. Rosenzweig suggests a more accurate way to think about leading a company, a robust and clearheaded approach that can save any business from ultimate failure. |
| ai in medical writing: Smart Healthcare Systems Adwitiya Sinha, Megha Rathi, 2019-07-24 About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions. |
| ai in medical writing: Future of Health Technology Renata Glowacka Bushko, 2002 This text provides a comprehensive vision of the future of health technology by looking at the ways to advance medical technologies, health information infrastructure and intellectual leadership. It also explores technology creations, adoption processes and the impact of evolving technologies. |
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| ai in medical writing: Medical Image Recognition, Segmentation and Parsing S. Kevin Zhou, 2015-12-11 This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: - Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects - Methods and theories for medical image recognition, segmentation and parsing of multiple objects - Efficient and effective machine learning solutions based on big datasets - Selected applications of medical image parsing using proven algorithms - Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects - Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets - Includes algorithms for recognizing and parsing of known anatomies for practical applications |
| ai in medical writing: The Complete Guide to Medical Writing Mark C. Stuart, 2007 'The Complete Guide to Medical Writing' is intended to consider all aspects of medical/scientific writing in one concise introductory text. It explains how to get published, how to write for a particular audience or in a particular media, what the publishing processes are and what the financial rewards might be. |
| ai in medical writing: Artificial Intelligence in Healthcare Parag Mahajan, 2021-02 ① Do you know what AI is doing to improve our health and wellbeing? ② Does this new technology concern you, or impress you? ③ Do you want to know more about the future of AI in healthcare? Technology continues to advance at a pace that can seem bewildering. Nowhere else is it moving faster than in the health sector, where ♥AI is now being used to improve millions of lives♥. In this book, ◆ Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone ◆, you can discover the great improvements that AI is making, with chapters covering: The current applications and future of AI in healthcare and all major medical specialties ✓ The benefits and risks weighed up ✓ The ethics involved ✓ Machine learning and data science simplified ✓ AI's role in medical research and education, health insurance, drug discovery, electronic health records, and the fight against COVID-19 ✓ The roles that major corporations and start-up companies are playing ✓ The implementation of AI in clinical practice ✓ And lots more... Quite simply the most authoritative text on the subject, Artificial Intelligence in Healthcare - 3rd Edition, is an absorbing and compelling read for anyone who wants to know more. It is packed with more updated information than any other book currently available, written in easy-to-understand language, and accessible to all. |
| ai in medical writing: Artificial Intelligence In Medicine Peter Szolovits, 1982-08-04 |
| ai in medical writing: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| ai in medical writing: Utilizing AI Tools in Academic Research Writing Srivastava, Anugamini Priya, Agarwal, Sucheta, 2024-05-02 Those entrenched in academia often have daunting processes of formulating research questions, data collection, analysis, and scholarly paper composition. Artificial intelligence (AI) emerges as an invaluable ally, simplifying these processes and elevating the quality of scholarly output. Where the pursuit of knowledge meets the cutting edge of technology, Utilizing AI Tools in Academic Research Writing unfolds a transformative journey through the symbiotic relationship between AI and academic inquiry. It offers practical insights into the myriad ways AI can revolutionize academic pursuits. This book extends beyond theoretical discussions, delving into practical dimensions of AI integration, demonstrating how it facilitates topic identification, refines research design, empowers data analysis, and enriches literature reviews. Readers will explore AI's indispensable role in precise hypothesis development, enhancing the very foundation of academic inquiry. The book introduces AI-powered tools that streamline writing and editing, ensuring research papers meet the highest standards of clarity and correctness. Ethical considerations in AI-integrated research take center stage, emphasizing responsible and transparent practices. This book is ideal for doctoral candidates, master's students, undergraduates, or seasoned faculty members. |
| ai in medical writing: Artificial Intelligence for COVID-19 Diego Oliva, Said Ali Hassan, Ali Mohamed, 2021-07-19 This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations. |
| ai in medical writing: Intelligent Decision Support Systems—A Journey to Smarter Healthcare Smaranda Belciug, Florin Gorunescu, 2019-03-20 The goal of this book is to provide, in a friendly and refreshing manner, both theoretical concepts and practical techniques for the important and exciting field of Artificial Intelligence that can be directly applied to real-world healthcare problems. Healthcare – the final frontier. Lately, it seems like Pandora opened the box and evil was released into the world. Fortunately, there was one thing left in the box: hope. In recent decades, hope has been increasingly represented by Intelligent Decision Support Systems. Their continuing mission: to explore strange new diseases, to seek out new treatments and drugs, and to intelligently manage healthcare resources and patients. Hence, this book is designed for all those who wish to learn how to explore, analyze and find new solutions for the most challenging domain of all time: healthcare. |
| ai in medical writing: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| ai in medical writing: The Artist in the Machine Arthur I. Miller, 2019-10-01 An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans. Today's computers are composing music that sounds “more Bach than Bach,” turning photographs into paintings in the style of Van Gogh's Starry Night, and even writing screenplays. But are computers truly creative—or are they merely tools to be used by musicians, artists, and writers? In this book, Arthur I. Miller takes us on a tour of creativity in the age of machines. Miller, an authority on creativity, identifies the key factors essential to the creative process, from “the need for introspection” to “the ability to discover the key problem.” He talks to people on the cutting edge of artificial intelligence, encountering computers that mimic the brain and machines that have defeated champions in chess, Jeopardy!, and Go. In the central part of the book, Miller explores the riches of computer-created art, introducing us to artists and computer scientists who have, among much else, unleashed an artificial neural network to create a nightmarish, multi-eyed dog-cat; taught AI to imagine; developed a robot that paints; created algorithms for poetry; and produced the world's first computer-composed musical, Beyond the Fence, staged by Android Lloyd Webber and friends. But, Miller writes, in order to be truly creative, machines will need to step into the world. He probes the nature of consciousness and speaks to researchers trying to develop emotions and consciousness in computers. Miller argues that computers can already be as creative as humans—and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature. |
| ai in medical writing: Mistreated Robert Pearl, 2017-05-02 The biggest problem in American health care is us Do you know how to tell good health care from bad health care? Guess again. As patients, we wrongly assume the best care is dependent mainly on the newest medications, the most complex treatments, and the smartest doctors. But Americans look for health-care solutions in the wrong places. For example, hundreds of thousands of lives could be saved each year if doctors reduced common errors and maximized preventive medicine. For Dr. Robert Pearl, these kinds of mistakes are a matter of professional importance, but also personal significance: he lost his own father due in part to poor communication and treatment planning by doctors. And consumers make costly mistakes too: we demand modern information technology from our banks, airlines, and retailers, but we passively accept last century's technology in our health care. Solving the challenges of health care starts with understanding these problems. Mistreated explains why subconscious misperceptions are so common in medicine, and shows how modifying the structure, technology, financing, and leadership of American health care could radically improve quality outcomes. This important book proves we can overcome our fears and faulty assumptions, and provides a roadmap for a better, healthier future. |
| ai in medical writing: Effective Medical Writing Thomas A. Buckingham, Phd Thomas a Buckingham MD, 2017-10-07 This book is a complete and clear guide that will help you format your ideas, clinical observations, and research into articles, book chapters, and review papers ready for publication. Medical writing skills are essential for today's clinician or researcher. Successful publication of your scientific work can have a surprising and enriching effect on your career. Dr. Thomas Buckingham, a distinguished writer and researcher, explains the basics of medical writing in an easy to read style. In depth discussions of how to write research papers, book chapters, review articles, editorials, letters to the editor, abstracts for scientific meetings, and case reports are included. This book is loaded with practical and useful information including tips on how to get your paper accepted for publication. Effective Medical Writing will help all authors improve their writing and publishing skills. |
| ai in medical writing: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| ai in medical writing: Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice Daniel A. Hashimoto, Guy Rosman, Ozanan R. Meireles, 2021-03-08 Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI. |
| ai in medical writing: Machine Learning and AI for Healthcare Arjun Panesar, 2019-02-04 Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings. |
| ai in medical writing: The Alignment Problem: Machine Learning and Human Values Brian Christian, 2020-10-06 A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful. |
| ai in medical writing: Machine Learning Ethem Alpaydin, 2016-10-07 A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security. |
| ai in medical writing: Medical Statistics Made Easy Michael Harris, Gordon Taylor, 2003-12-05 It is not necessary to know how to do a statistical analysis to critically appraise a paper. However, it is necessary to have a grasp of the basics, of whether the right test has been used and how to interpret the resulting figures. Short, readable, and useful, this book provides the essential, basic information without becoming bogged down in the |
| ai in medical writing: Pharmako-AI K. Allado-McDowell, 2020 This book collects essays, stories, and poems ... [the author] wrote with OpenAI's GPT-3 language model, a neural net that generates text sequences--Page xi. |
| ai in medical writing: The Cambridge Handbook of Artificial Intelligence Keith Frankish, William M. Ramsey, 2014-06-12 An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists. |
| ai in medical writing: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| ai in medical writing: The Creativity Code Marcus Du Sautoy, 2020-03-03 “A brilliant travel guide to the coming world of AI.” —Jeanette Winterson What does it mean to be creative? Can creativity be trained? Is it uniquely human, or could AI be considered creative? Mathematical genius and exuberant polymath Marcus du Sautoy plunges us into the world of artificial intelligence and algorithmic learning in this essential guide to the future of creativity. He considers the role of pattern and imitation in the creative process and sets out to investigate the programs and programmers—from Deep Mind and the Flow Machine to Botnik and WHIM—who are seeking to rival or surpass human innovation in gaming, music, art, and language. A thrilling tour of the landscape of invention, The Creativity Code explores the new face of creativity and the mysteries of the human code. “As machines outsmart us in ever more domains, we can at least comfort ourselves that one area will remain sacrosanct and uncomputable: human creativity. Or can we?...In his fascinating exploration of the nature of creativity, Marcus du Sautoy questions many of those assumptions.” —Financial Times “Fascinating...If all the experiences, hopes, dreams, visions, lusts, loves, and hatreds that shape the human imagination amount to nothing more than a ‘code,’ then sooner or later a machine will crack it. Indeed, du Sautoy assembles an eclectic array of evidence to show how that’s happening even now.” —The Times |
| ai in medical writing: Application of Artificial Intelligence to Assessment Hong Jiao, Robert W. Lissitz, 2020-03-01 The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices. |
| ai in medical writing: Studying Those Who Study Us Diana Forsythe, 2001 Diana E. Forsythe was a leading anthropologist of science, technology, and work who pioneered the field of the anthropology of artificial intelligence. This volume collects her best-known essays, along with other major works that remained unpublished upon her death in 1997. It is also an exemplar of how reflexive ethnography should be done. |
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