The Doctor And The Algorithm

For years, technologists and computer scientists have promised an AI revolution that would transform the very basis of how we imagine and administer modern medicine. AI-driven advancements in medical error rates, diagnostic accuracy, or disease outbreak detection could potentially save thousands of lives. But health AI also carries the potential for exacerbating deep systemic biases if left unchecked. The Doctor and the Algorithm combines insights from science and technology studies, critical algorithm studies, and public interest informatics to better understand the promise and peril of health AI. The book draws on case studies in automated diagnostics, algorithmic pain measurement, AI-driven drug discovery, and death prediction to investigate how health AI is made, promoted, and justified. It explores the enthusiastic promises of health AI marketing communication and medical futurism while also analyzing the inequitable outcomes new AI technology often creates for already marginalized communities. Finally, the book closes with specific recommendations for regulatory frameworks that might support more ethical and equitable approaches to health AI in the future. Interweaving textual analysis and original informatics, The Doctor and the Algorithm offers a sobering analysis of the promise of medical AI against the real and unintended consequences that deep medicine can bring for patients, providers, and public health alike.

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

Algorithms serve an important purpose in the field of psychopharmacology as heuristics for avoiding the biases and cognitive lapses that are common when prescribing for many conditions whose treatment is based on complex data. Unique in the field, this title compiles twelve papers from the Psychopharmacology Algorithm Project at the Harvard South Shore Psychiatry Residency Training Program and presents practical ways to adopt evidence-based practices into the day-to-day treatment of patients. Psychopharmacology Algorithms is a useful resource for practicing psychiatrists, residents, and fellows, as well as psychiatric nurse practitioners, psychiatric physician assistants who prescribe, advanced practice pharmacists who prescribe, and primary care clinicians. Teachers of psychopharmacology may find it particularly valuable. Researchers in clinical psychopharmacology may find it helpful in identifying important practice areas that are in need of further study.

A professor of medicine reveals how technology like wireless internet, individual data, and personal genomics can be used to save lives.

Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author

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.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Medicine stands at a crossroads. Artificial intelligence (AI) offers unprecedented power to diagnose diseases, personalize treatments, and revolutionize healthcare. But can we trust these complex algorithms with our health? M.B. Chatfield and Ava Arin delves into the doctor's dilemma in the age of AI. This thought-provoking book explores the ethical and practical challenges of integrating AI into healthcare. You'll learn about: The immense potential of AI for faster diagnoses, targeted treatments, and preventative care. The critical questions surrounding bias, transparency, and the human element in healthcare decisions. The evolving role of doctors – how AI can augment their expertise, not replace it. Trust or Technology is a compelling read for anyone who wants to understand the future of medicine. It's a call for a balanced approach, where cutting-edge technology empowers doctors to deliver the best possible care, while human compassion remains at the heart of healing. Order today and be among the first to navigate this new era of healthcare!

A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.