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AI in Healthcare Case Study: Best Practices and Common Pitfalls
Author: Dr. Evelyn Reed, PhD, Chief Data Scientist at HealthTech Innovations, with 15 years of experience in applying AI to medical imaging and diagnostics.
Publisher: HealthData Insights, a leading publisher of research and analysis on the application of data science and AI in healthcare. HealthData Insights provides insightful reports and analysis for healthcare professionals, researchers, and investors.
Editor: Dr. Michael Chen, MD, PhD, a medical doctor with a PhD in bioinformatics and extensive experience in peer-reviewing research on AI in healthcare.
Summary: This comprehensive guide on "AI in healthcare case study" explores successful implementations and common challenges in leveraging artificial intelligence within the healthcare sector. It outlines best practices for designing, executing, and analyzing AI case studies, emphasizing ethical considerations, data privacy, and regulatory compliance. The guide also details frequent pitfalls to avoid, offering practical advice for researchers and healthcare professionals seeking to maximize the impact of AI in improving patient care and optimizing healthcare systems.
Keywords: AI in healthcare case study, AI in medicine case study, machine learning in healthcare case study, deep learning in healthcare case study, AI healthcare applications, AI case studies healthcare, best practices AI healthcare, pitfalls AI healthcare, ethical considerations AI healthcare, regulatory compliance AI healthcare.
1. Introduction: The Growing Importance of AI in Healthcare Case Studies
The application of Artificial Intelligence (AI) in healthcare is rapidly evolving, offering transformative potential across various domains, from diagnostics and treatment planning to drug discovery and personalized medicine. Robust and well-documented "AI in healthcare case studies" are crucial for demonstrating the efficacy, safety, and ethical implications of these technologies. This guide provides a framework for conducting and analyzing such case studies, highlighting best practices and common pitfalls.
2. Defining the Scope of Your AI in Healthcare Case Study
Before embarking on an "AI in healthcare case study," clearly define the research question, objectives, and scope. This includes identifying the specific AI technology being evaluated (e.g., machine learning, deep learning, natural language processing), the target medical application (e.g., image analysis, risk prediction, clinical decision support), and the relevant patient population. A well-defined scope ensures a focused and impactful study.
3. Data Acquisition and Preprocessing for AI in Healthcare Case Studies
Data forms the bedrock of any successful "AI in healthcare case study." This phase requires careful consideration of data sources, ensuring data quality, addressing biases, and maintaining patient privacy and confidentiality in strict accordance with HIPAA and GDPR regulations. Data preprocessing, including cleaning, transformation, and feature engineering, is crucial for optimal model performance. Robust data governance is paramount.
4. Model Development and Validation in AI in Healthcare Case Studies
The choice of AI model should be aligned with the research question and the characteristics of the data. Rigorous model validation is essential, employing techniques like cross-validation, bootstrapping, and independent testing sets to ensure generalizability and avoid overfitting. Transparency in model development and deployment is critical for trust and accountability.
5. Ethical Considerations in AI in Healthcare Case Studies
Ethical considerations are paramount in "AI in healthcare case studies." Address issues of bias, fairness, transparency, accountability, and privacy. Obtain informed consent from patients, ensuring data anonymity and security. Consider the potential impact of AI on healthcare disparities and strive for equitable access to AI-powered solutions.
6. Regulatory Compliance in AI in Healthcare Case Studies
Navigating the regulatory landscape is vital. Adherence to regulations like HIPAA (in the US) and GDPR (in Europe) is crucial. Transparency regarding data usage, model validation, and clinical impact is essential for regulatory approval and patient trust. Collaborate with legal and regulatory experts to ensure compliance.
7. Common Pitfalls in AI in Healthcare Case Studies
Several common pitfalls can undermine the validity and impact of "AI in healthcare case studies." These include: inadequate data quality, biased datasets, inappropriate model selection, lack of validation, insufficient attention to ethical considerations, and neglecting regulatory compliance. Careful planning and meticulous execution can mitigate these risks.
8. Best Practices for Conducting Successful AI in Healthcare Case Studies
Successful "AI in healthcare case studies" are characterized by rigorous methodology, transparent reporting, and a focus on clinical impact. Key best practices include: clearly defined research questions, robust data management, rigorous model validation, ethical considerations, regulatory compliance, and collaboration with multidisciplinary teams (clinicians, data scientists, ethicists, regulators).
9. Analyzing and Reporting the Results of your AI in Healthcare Case Study
Once the study is complete, rigorously analyze the results, focusing on both the performance metrics of the AI model and the clinical implications of its application. Clearly report findings, including limitations, and disseminate the results through peer-reviewed publications and presentations at relevant conferences.
Conclusion
Conducting impactful "AI in healthcare case studies" requires careful planning, meticulous execution, and a commitment to ethical principles and regulatory compliance. By following best practices and avoiding common pitfalls, researchers can generate robust evidence supporting the safe and effective integration of AI into healthcare systems, ultimately improving patient outcomes and transforming healthcare delivery.
FAQs
1. What are the key performance indicators (KPIs) for evaluating AI in healthcare? KPIs vary depending on the application, but common examples include accuracy, sensitivity, specificity, AUC, precision, recall, and F1-score for diagnostic models; and cost-effectiveness, efficiency gains, and patient satisfaction for operational applications.
2. How can I ensure data privacy and security in my AI in healthcare case study? Implement robust data anonymization techniques, secure data storage and access controls, and comply with relevant regulations like HIPAA and GDPR.
3. What are the ethical challenges of using AI in healthcare? Ethical challenges include bias in algorithms, potential for job displacement, lack of transparency and explainability, and the impact on patient autonomy.
4. What is the role of explainable AI (XAI) in healthcare case studies? XAI techniques help to understand how an AI model makes decisions, improving trust and accountability.
5. How can I address potential bias in my AI in healthcare dataset? Carefully examine your data for imbalances and biases, employ techniques like resampling and reweighting, and consider using fairness-aware algorithms.
6. What are the regulatory hurdles for deploying AI in healthcare? Regulations vary by country but often involve clinical validation, safety assessments, and data privacy compliance.
7. How can I collaborate effectively with clinicians in an AI in healthcare project? Establish clear communication channels, involve clinicians in all stages of the project, and prioritize clinical relevance and usability.
8. What are some examples of successful AI in healthcare case studies? Successful case studies exist in areas like image analysis for cancer detection, risk prediction for chronic diseases, and personalized medicine.
9. Where can I find datasets for conducting AI in healthcare research? Several public repositories provide datasets for AI in healthcare research, including MIMIC-III, UCI Machine Learning Repository, and The Cancer Imaging Archive (TCIA).
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| ai in healthcare case study: 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 |
| ai in healthcare case study: Explainable AI in Healthcare and Medicine Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge, 2020-11-02 This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence. |
| ai in healthcare case study: Handbook of Artificial Intelligence in Healthcare , 2022 |
| ai in healthcare case study: AI for Healthcare with Keras and Tensorflow 2.0 Anshik, 2021 Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask. By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. You will: Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies Look at different problem areas within the healthcare industry and solve them in a code-first approach Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks Understand the industry and learn ML . |
| ai in healthcare case study: Artificial Intelligence in Behavioral and Mental Health Care David D. Luxton, 2015-09-10 Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings |
| ai in healthcare case study: 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 healthcare case study: Deep Medicine Eric Topol, 2019-03-12 A Science Friday pick for book of the year, 2019 One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved. |
| ai in healthcare case study: 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. |
| ai in healthcare case study: Clinical Case Studies in Home Health Care Leslie Neal-Boylan, 2011-11-22 Home health care is an important aspect of community health and a growing area of healthcare services. Clinical Case Studies in Home Health Care uses a case-based approach to provide home healthcare professionals, educators, and students with a useful tool for thoughtful, holistic care. The book begins with a thorough and accessible introduction to the principles of home health care, including a discussion of supporting theoretical frameworks and information on managing complexities, transitioning patients to home care, and preparation for the home visit. Subsequent sections are comprised entirely of case studies organized by body system. Though cases are diverse in content, each is presented in a consistent manner, incorporating relevant data about the patient and caregivers and the approach to patient care and promoting a logical approach to patient presentation. Cases also include helpful tips about reimbursement practices, cultural competence, community resources, and rehabilitation needs. |
| ai in healthcare case study: 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 healthcare case study: Smart Systems for Industrial Applications C. Venkatesh, N. Rengarajan, P. Ponmurugan, S. Balamurugan, 2022-01-07 SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc. Audience The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others. |
| ai in healthcare case study: 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. |
| ai in healthcare case study: Machine Learning and AI for Healthcare Arjun Panesar, 2020-12-25 This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things. You will understand 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. What You Will Learn Understand key machine learning algorithms and their use and implementation within healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Manage the complexities of massive data Be familiar with AI and healthcare best practices, feedback loops, and intelligent agents Who 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 healthcare case study: Philosophy and Theory of Artificial Intelligence Vincent C. Müller, 2012-08-23 Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. |
| ai in healthcare case study: Handbook of Artificial Intelligence in Healthcare Chee-Peng Lim, Ashlesha Vaidya, Kiran Jain, Virag U. Mahorkar, Lakhmi C. Jain, 2022 This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context. The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans. In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector. |
| ai in healthcare case study: Cases in Hospital Medicine Zahir Kanjee, Joshua M. Liao, 2019-10-16 Written by authors who are hospitalists and clinician-educators, Cases in Hospital Medicine uses practical case studies and current medical evidence to guide you expertly through the types of cases seen most often by practicing hospital-based clinicians. This engaging handbook covers the wide range of both broad and specific knowledge required in the hospital environment, while focusing on highly relevant questions and today’s best practices. You’ll find real-world guidance on essential topics, including commentary on research studies and clinical guidelines.\ |
| ai in healthcare case study: Healthcare and Artificial Intelligence Bernard Nordlinger, Cédric Villani, Daniela Rus, 2020-03-17 This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic “AI” might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details. |
| ai in healthcare case study: Artificial Intelligence Sandeep Reddy, 2020-12-02 The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery. Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to healthcare delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration, and delivery and how they can commence applying AI in their health services. Researchers and technology professionals will also find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains. |
| ai in healthcare case study: Artificial Intelligence in Healthcare Anusha Kostka, 2024-05-19 Artificial Intelligence in Healthcare: A Compilation of Case Studies offers a comprehensive view of AI's transformative impact on healthcare. Delving into applications and challenges, the book showcases detailed case studies illuminating AI's role in diagnosis, treatment, prevention, and management. From early cancer detection to personalized medicine, each case study exemplifies AI's potential to improve patient outcomes. Ethical dilemmas and emerging trends in AI research are also explored, providing a holistic view of AI's future in healthcare. This book is an essential guide for researchers, healthcare professionals, and anyone intrigued by AI's potential to revolutionize healthcare delivery. |
| ai in healthcare case study: Intelligence-Based Healthcare Anthony Chang, Alfonso Limon, 2025-10-01 Intelligence-based healthcare: An essential guide with case studies of artificial intelligence for the healthcare leader and provider is an essential guide with an introduction to data science and artificial intelligence to provide the reader with a quick orientation and education on AI in healthcare. This book also offers a framework for success in planning, deployment, implementation, and evaluation of AI models in healthcare. In 25 chapters Intelligence-based healthcare: An essential guide with case studies of artificial intelligence for the healthcare leader and provider both introduces the reader to artificial in medicine and healthcare, and AI concepts. To render AI more understandable and relatable to the reader, case studies are used as a learning strategy to illustrate the aforementioned AI concepts. The cases implement AI in solving a very specific problem, clinical or operational, in clinical medicine or healthcare. Both cases that illustrate successful implementation of AI as unsuccessful application cases with important lessons learned are included. Each chapter can be read independently and therefore the book is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of artificial intelligence in medicine and healthcare, and all those who wish to broaden their knowledge in the allied field.• Provides access to research of front-line leaders of close to 100 centers of AI in medicine from around the world• Couples concepts of artificial intelligence and applications of these AI tools in clinical medicine and healthcare that is not overly technical but synergistic is unique• Presents case studies in a systematic manner for all stakeholders to understand the in-depth thinking is a first-of-its-kind book to render AI much more relatable and transparent |
| ai in healthcare case study: Digital Infrastructure for the Learning Health System Institute of Medicine, Roundtable on Value and Science-Driven Health Care, 2011-10-21 Like many other industries, health care is increasingly turning to digital information and the use of electronic resources. The Institute of Medicine's Roundtable on Value & Science-Driven Health Care hosted three workshops to explore current efforts and opportunities to accelerate progress in improving health and health care with information technology systems. |
| ai in healthcare case study: Artificial Intelligence for Business Ana Landeta Echeberria, 2022-01-22 This book seeks to build a shared understanding of Artificial Intelligence (AI) within the global business scenario today and in the near future. Drawing on academic theory and real-world case studies, it examines AI’s development and application across a number of business contexts. Taking current scholarship forward in its engagement with AI theory and practice for enterprises and applied research and innovation, it outlines international practices for the promotion of reliable AI systems, trends, research and development, fostering a digital ecosystem for AI and preparing companies for job transformation and building skills. This book will be of great interest to academics studying Digital Business, Digital Strategy, Innovation Management, and Information Technology. |
| ai in healthcare case study: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
| ai in healthcare case study: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. |
| ai in healthcare case study: Clinical Reasoning in the Health Professions Joy Higgs, Mark A Jones, Stephen Loftus, PhD, MSc, BDS, Nicole Christensen, 2008-02-14 Clinical reasoning is the foundation of professional clinical practice. Totally revised and updated, this book continues to provide the essential text on the theoretical basis of clinical reasoning in the health professions and examines strategies for assisting learners, scholars and clinicians develop their reasoning expertise. key chapters revised and updated nature of clinical reasoning sections have been expanded increase in emphasis on collaborative reasoning core model of clinical reasoning has been revised and updated |
| ai in healthcare case study: Artificial Intelligence in Healthcare and Medicine Kayvan Najarian, Delaram Kahrobaei, Enrique Dominguez, Reza Soroushmehr, 2022-04-06 This book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor. |
| ai in healthcare case study: Computer Vision In Medical Imaging Chi Hau Chen, 2013-11-18 The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs. |
| ai in healthcare case study: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| ai in healthcare case study: Internet of Medical Things D. Jude Hemanth, J. Anitha, George A. Tsihrintzis, 2021-04-13 This book looks at the growing segment of Internet of Things technology (IoT) known as Internet of Medical Things (IoMT), an automated system that aids in bridging the gap between isolated and rural communities and the critical healthcare services that are available in more populated and urban areas. Many technological aspects of IoMT are still being researched and developed, with the objective of minimizing the cost and improving the performance of the overall healthcare system. This book focuses on innovative IoMT methods and solutions being developed for use in the application of healthcare services, including post-surgery care, virtual home assistance, smart real-time patient monitoring, implantable sensors and cameras, and diagnosis and treatment planning. It also examines critical issues around the technology, such as security vulnerabilities, IoMT machine learning approaches, and medical data compression for lossless data transmission and archiving. Internet of Medical Things is a valuable reference for researchers, students, and postgraduates working in biomedical, electronics, and communications engineering, as well as practicing healthcare professionals. |
| ai in healthcare case study: 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 healthcare case study: Accelerated Path to Cures Josep Bassaganya-Riera, 2018 Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical and clinical experimentation to accelerate drug development. This book discusses the application of advanced modeling technologies, from target identification and validation to nonclinical studies in animals to Phase 1-3 human clinical trials and post-approval monitoring, as alternative models of drug development. As a case of successful integration of computational modeling and drug development, we discuss the development of oral small molecule therapeutics for inflammatory bowel disease, from the application of docking studies to screening new chemical entities to the development of next-generation in silico human clinical trials from large-scale clinical data. Additionally, this book illustrates how modeling techniques, machine learning, and informatics can be utilized effectively at each stage of drug development to advance the progress towards predictive, preventive, personalized, precision medicine, and thus provide a successful framework for Path to Cures. |
| ai in healthcare case study: 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 |
| ai in healthcare case study: Artificial Intelligence and Machine Learning in Public Healthcare KC Santosh, Loveleen Gaur, 2022-01-01 This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions. |
| ai in healthcare case study: The Oxford Handbook of Stigma, Discrimination, and Health Brenda Major, John F. Dovidio, Bruce G. Link, 2018 Stigma leads to poorer health. In The Oxford Handbook of Stigma, Discrimination, and Health, leading scholars identify stigma mechanisms that operate at multiple levels to erode the health of stigmatized individuals and, collectively, produce health disparities. This book provides unique insights concerning the link between stigma and health across various types of stigma and groups. |
| ai in healthcare case study: Artificial Intelligence and Machine Learning in Healthcare Ankur Saxena, Shivani Chandra, 2021-05-06 This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare. |
| ai in healthcare case study: ARTIFICIAL INTELLIGENCE IN HEALTHCARE INDRA REDDY MALLELA SANDHYARANI GANIPANENI SRINIVASULU HARSHAVARDHAN KENDYALA SHALU JAIN , 2024-10-17 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Artificial Intelligence in Healthcare: Innovations, Challenges, and Future Perspectives, is conceived to bridge the gap between emerging technological advancements in artificial intelligence and their strategic application in healthcare management. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of AI technologies, particularly in the healthcare sector, and their integration into medical practices. From foundational theories to advanced applications, we delve into the critical aspects that drive successful innovation in healthcare environments. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, healthcare professionals, and industry leaders. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world healthcare scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and AI adoption to the strategic management of healthcare innovation. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that AI technologies and healthcare management play in shaping the future of medical services. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how AI technologies and healthcare management can be harnessed together to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will define the future of healthcare. Thank you for joining us on this journey. Authors |
| ai in healthcare case study: Artificial Intelligence in Healthcare Lalit Garg, Sebastian Basterrech, Chitresh Banerjee, Tarun K. Sharma, 2021-10-29 This book highlights the analytics and optimization issues in healthcare systems, proposes new approaches, and presents applications of innovative approaches in real facilities. In the past few decades, there has been an exponential rise in the application of swarm intelligence techniques for solving complex and intricate problems arising in healthcare. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this book is to bring forward thorough, in-depth, and well-focused developments of hybrid variants of swarm intelligence algorithms and their applications in healthcare systems. |
| ai in healthcare case study: Machine Learning , 2017 |
| ai in healthcare case study: Pain Management and the Opioid Epidemic National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Committee on Pain Management and Regulatory Strategies to Address Prescription Opioid Abuse, 2017-09-28 Drug overdose, driven largely by overdose related to the use of opioids, is now the leading cause of unintentional injury death in the United States. The ongoing opioid crisis lies at the intersection of two public health challenges: reducing the burden of suffering from pain and containing the rising toll of the harms that can arise from the use of opioid medications. Chronic pain and opioid use disorder both represent complex human conditions affecting millions of Americans and causing untold disability and loss of function. In the context of the growing opioid problem, the U.S. Food and Drug Administration (FDA) launched an Opioids Action Plan in early 2016. As part of this plan, the FDA asked the National Academies of Sciences, Engineering, and Medicine to convene a committee to update the state of the science on pain research, care, and education and to identify actions the FDA and others can take to respond to the opioid epidemic, with a particular focus on informing FDA's development of a formal method for incorporating individual and societal considerations into its risk-benefit framework for opioid approval and monitoring. |
| ai in healthcare case study: Machine Learning with Health Care Perspective Vishal Jain, Jyotir Moy Chatterjee, 2020-03-09 This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges. |
Artificial Intelligence in Healthcare: Review and Prediction …
In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assis- tance, biomedical information processing, and …
Case study: Intermountain Healthcare — AI-powered …
Intermountain Healthcare launched an ambitious initiative for clinical documentation integrity (CDI) to move beyond manual . processes and leverage technology to integrate workflows, automate …
Exemplars of Artificial Intelligence and Machine Learning in …
We wrote this report for clinicians, health service executives, academics and others with an interest in using data to improve today’s health service, and in understanding how AI and ML …
AI-powered healthcare: Shaping the future of population …
Generative AI revolutionises population health management by translating insights into targeted, precise interventions and care plans across the spectrum of needs for an individual—from …
AI in healthcare study - Definitive Healthcare
Definitive Healthcare has studied the changing role of AI and ML in healthcare organizations over the last few years, uncovering important trends and insights.
AUTOMATED HEALTHCARE APP - Princeton Dialogues on AI …
4 | AI Ethics Case - Automated Healthcare App and machine learning capabilities, Charlie was able to predict which users were least likely to comply with the app’s healthcare …
AI in healthcare case study - classroom.eneri.eu
The use of AI federated learning models in healthcare raises several ethical concerns around cybersecurity and data privacy. Federated learning allows AI systems to train on decentralised …
Human-AI Interaction in Healthcare: Three Case Studies About …
evaluate the systems supporting human-AI interaction in the healthcare domain. Collaborating with the local government administrators, hospitals, clinics and doctors, we get a valuable …
Case Study: Hospital Uses AI to Access New Data—and …
The unprecedented demands placed on healthcare providers during the COVID pandemic underscored an increasingly acute need for medical teams: faster access to more and more …
Artificial Intelligence in Healthcare - Accenture
To better understand the savings potential of AI, Accenture analyzed a comprehensive taxonomy of 10 AI applications with the greatest near-term impact in healthcare. The assessment defined …
AI Use Cases for Healthcare - UiPath
Based on UiPath AI Fabric, we’ve built a computer vision model for detecting COVID-19 cases from X-ray chest images in seconds while usually, it can take up to 20 minutes. Learn more …
Transforming healthcare with AI - McKinsey & Company
the impact of AI on healthcare practitioners, and the implications of introducing and scaling AI for healthcare organisations and healthcare systems, with a particular focus on Europe and EU …
Case study Using imaging and AI to help diagnose and …
• AI-supported imaging tools can help hospitals undertake better planning. For example in bed/ward allocations and the need for ventilators and other equipment. They could help plan...
Artificial Intelligence for Healthcare - Chatham House
Jul 30, 2020 · This paper describes some of the main opportunities and challenges of using AI in healthcare. It then turns to a case study of the use of AI for healthcare purposes in India, …
Challenges for Responsible AI Design and Workflow …
Our work presents a rare example of an in-depth case study that engages early in the AI design process with the end-to-end workflow and concrete use context of CXR-based NGT placement …
The use of AI in healthcare - recipes-project.eu
The aim of this case study is to better understand the complexities and controversies for applying the precautionary principle to the use of artificial intelligence (AI) in healthcare. The case …
Big Data, Analytics & Artificial Intelligence - GE Healthcare
Case Study: A Library of Deep Learning Algorithms to Advance Care Globally At the University of California San Francisco (UCSF), clinicians are working in partnership with GE Healthcare to …
Artificial Intelligence and Discrimination in Health Care
Artificial intelligence (AI) holds great promise for improved health-care outcomes. It has been used to analyze tumor images, to help doctors choose among different treatment options, and to …
The Role of Artificial Intelligence in Transforming Healthcare: …
AI can assist in diagnosing disease, predicting outcomes, discovering new drugs, enabling precise and efficient care, optimizing workflows, and reducing errors. A recent survey of hospitals …
Artificial Intelligence in Healthcare: Review and Prediction …
In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assis- tance, biomedical information processing, and …
Case study: Intermountain Healthcare — AI-powered …
Intermountain Healthcare launched an ambitious initiative for clinical documentation integrity (CDI) to move beyond manual . processes and leverage technology to integrate workflows, …
Exemplars of Artificial Intelligence and Machine Learning in …
We wrote this report for clinicians, health service executives, academics and others with an interest in using data to improve today’s health service, and in understanding how AI and ML …
AI-powered healthcare: Shaping the future of population …
Generative AI revolutionises population health management by translating insights into targeted, precise interventions and care plans across the spectrum of needs for an individual—from …
A Case Study on Artificial Intelligence Application in Medical …
the use of AI chatbots that is AI form of doctor and a UK based digital healthcare organisation is studying this collaboration of patients with AI doctors.
AI in healthcare study - Definitive Healthcare
Definitive Healthcare has studied the changing role of AI and ML in healthcare organizations over the last few years, uncovering important trends and insights.
AUTOMATED HEALTHCARE APP - Princeton Dialogues on AI …
4 | AI Ethics Case - Automated Healthcare App and machine learning capabilities, Charlie was able to predict which users were least likely to comply with the app’s healthcare …
AI in healthcare case study - classroom.eneri.eu
The use of AI federated learning models in healthcare raises several ethical concerns around cybersecurity and data privacy. Federated learning allows AI systems to train on decentralised …
Human-AI Interaction in Healthcare: Three Case Studies …
evaluate the systems supporting human-AI interaction in the healthcare domain. Collaborating with the local government administrators, hospitals, clinics and doctors, we get a valuable …
Case Study: Hospital Uses AI to Access New Data—and …
The unprecedented demands placed on healthcare providers during the COVID pandemic underscored an increasingly acute need for medical teams: faster access to more and more …
Artificial Intelligence in Healthcare - Accenture
To better understand the savings potential of AI, Accenture analyzed a comprehensive taxonomy of 10 AI applications with the greatest near-term impact in healthcare. The assessment …
AI Use Cases for Healthcare - UiPath
Based on UiPath AI Fabric, we’ve built a computer vision model for detecting COVID-19 cases from X-ray chest images in seconds while usually, it can take up to 20 minutes. Learn more …
Transforming healthcare with AI - McKinsey & Company
the impact of AI on healthcare practitioners, and the implications of introducing and scaling AI for healthcare organisations and healthcare systems, with a particular focus on Europe and EU …
Case study Using imaging and AI to help diagnose and …
• AI-supported imaging tools can help hospitals undertake better planning. For example in bed/ward allocations and the need for ventilators and other equipment. They could help plan...
Artificial Intelligence for Healthcare - Chatham House
Jul 30, 2020 · This paper describes some of the main opportunities and challenges of using AI in healthcare. It then turns to a case study of the use of AI for healthcare purposes in India, …
Challenges for Responsible AI Design and Workflow …
Our work presents a rare example of an in-depth case study that engages early in the AI design process with the end-to-end workflow and concrete use context of CXR-based NGT placement …
The use of AI in healthcare - recipes-project.eu
The aim of this case study is to better understand the complexities and controversies for applying the precautionary principle to the use of artificial intelligence (AI) in healthcare. The case …
Big Data, Analytics & Artificial Intelligence - GE Healthcare
Case Study: A Library of Deep Learning Algorithms to Advance Care Globally At the University of California San Francisco (UCSF), clinicians are working in partnership with GE Healthcare to …
Artificial Intelligence and Discrimination in Health Care
Artificial intelligence (AI) holds great promise for improved health-care outcomes. It has been used to analyze tumor images, to help doctors choose among different treatment options, and to …
The Role of Artificial Intelligence in Transforming Healthcare: …
AI can assist in diagnosing disease, predicting outcomes, discovering new drugs, enabling precise and efficient care, optimizing workflows, and reducing errors. A recent survey of hospitals …
