Recommended Reading about Artificial Intelligence (AI)

Faculty in the Section of Biomedical Informatics & Data Science have prepared this list of recommended reading about Artificial Intelligence.

Yale benefits from growing computational and AI expertise in the Section for Biomedical Informatics; the departments of Computer Science and Statistics and Data Science; the Program for Computational Biology and Bioinformatics; the Schmidt Program on Artificial Intelligence, Emerging Technologies, and National Power; the Center for Neurocomputation and Machine Intelligence; as well as other programs and units.

Perspectives - AI Recommended Reading

Perspective Title	Author	Summary
My North Star for the Future of AI	Fei-Fei Li	The most powerful companies in the world are shaping what artificial intelligence will become—but they’ll never get it right without the ethos and values of university scientists.
Seven technologies to watch in 2024	Michael Eisenstein	From protein engineering and 3D printing to detection of deepfake media, here are seven areas of technology that Nature will be watching in the year ahead.

Research Articles - AI Recommended Reading

Research Article Title	Author	Summary
Towards Conversational Diagnostic AI	Tao Tu, Anil Palepu, Mike Schaekermann	“Towards Conversational Diagnostic AI” presents AMIE, an AI system that can conduct clinical history-taking and diagnostic dialogue with patients. AMIE is based on a large language model that learns from a novel self-play environment with automated feedback. The paper evaluates AMIE’s performance on various aspects of medical consultation, such as diagnostic accuracy, management reasoning, communication skills, and empathy. The paper compares AMIE with primary care physicians in a randomized, double-blind study using text-based consultations with validated patient actors. The paper reports that AMIE outperformed the physicians on most of the evaluation metrics, according to specialist physicians and patient actors. -Yuval Kluger
The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 [Internet]	Microsoft Research AI4Science, Microsoft Azure Quantum	In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE).
Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions.	Overgaard SM, Graham MG, Brereton T, Pencina MJ, Halamka JD, Vidal DE, Economou-Zavlanos NJ	The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies within healthcare settings holds the potential to close the AI translation gap by establishing a robust framework that accelerates the safe, ethical, and effective delivery of AI/ML in day-to-day patient care.
Radiological Health. Good Machine Learning Practice for Medical Device Development.	U.S. Food and Drug Administration (FDA)	Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. They use software algorithms to learn from real-world use and in some situations may use this information to improve the product’s performance. But they also present unique considerations due to their complexity and the iterative and data-driven nature of their development.
Towards Generalist Biomedical AI	Tao Tu, Shekoofeh Azizi	Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery.
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023	Charlotte J. Haug, M.D., Ph.D., and Jeffrey M. Drazen, M.D.	Progress in data science is not simply a matter of increased performance, speed, and storage. In addition to the type of information found in librar- ies, data generated in organizations, and estab- lished systems designed to gather and codify data, new forms of technology can use data that are both people-generated and machine-generated. These data are often chaotic and unstructured.
Large Language Models in Medicine: The Potentials and Pitfalls	Jesutofunmi A. Omiye, MD, MS, Haiwen Gui, BS	This review, coupled with a tutorial, provides a comprehensive yet accessible overview of what LLMs are, their development, their current and potential applications, and the associated pitfalls in a medical setting, with the aim of familiarizing health care professionals with the rapidly changing landscape of LLMs in medicine.

Media Articles - AI Recommended Reading

Media Article Title	Author	Summary
How to Become an Expert on A.I.	Kevin Roose and Cade Metz	Part 1 of NYT series: Welcome to On Tech: A.I., a pop-up newsletter that will teach you about artificial intelligence, especially the new breed of chatbots like ChatGPT — all in only five days.

Books - AI Recommended Reading

Book Title	Author	Summary
Living with Robots: What Every Anxious Human Needs to Know	Ruth Aylett and Patricia A. Vargas	There’s a lot of hype about robots; some of it is scary and some of it utopian. In this accessible book, two robotics experts reveal the truth about what robots can and can’t do, how they work, and what we can reasonably expect their future capabilities to be. It will not only make you think differently about the capabilities of robots; it will make you think differently about the capabilities of humans. Living with Robots equips readers to look at robots concretely—as human-made artifacts rather than placeholders for our anxieties.
Working with AI: Real Stories of Human-Machine Collaboration	Thomas H. Davenport and Steven M. Miller	This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.
Ten Visions for Our Future	Kai-Fu Lee and Chen Qiufan	This book combines both science fiction and non-fiction to explore how AI will change our world by 2041. Kai-Fu Lee, a prominent AI expert, and Chen Qiufan, a science fiction writer, offer a unique blend of storytelling and analysis. The fictional stories, set in the year 2041, are penned by Chen Qiufan, while Kai-Fu Lee provides insights into how these scenarios might become reality. It’s a thought-provoking look at how AI could shape our future in diverse ways.
Artificial Intelligence: A Modern Approach	Stuart Russell and Peter Norvig	Artificial Intelligence: A Modern Approach is a comprehensive textbook on artificial intelligence. The book is designed to be accessible to students with a background in computer science and provides a broad overview of the field, covering a wide range of topics including search algorithms, machine learning, natural language processing, and robotics. The book is known for its clear and concise writing style, as well as its up-to-date coverage of the latest research and developments in AI. It is widely used as a textbook in university courses on AI and is considered a classic work in the field.