Janna Hastings is a computer scientist with more than a decade of experience across the life, behavioural and social sciences. She is also a data scientist with a PhD in computational biology and extensive experience in bioinformatics, cheminformatics and psychoinformatics. She is currently Assistant Professor of Medical Knowledge and Decision Support at the University of Zurich, and Vice-Director of the School of Medicine at the University of St. Gallen. Her current research focuses on bridging the gaps between knowledge and learning to bring AI technologies in medicine closer to the needs and workflows of clinicians and to support truly interdisciplinary and integrative knowledge discovery.

“An excellent summary of the state of the art of AI for Scientific Discovery. A concise and informative book covering the main areas of the topic. It is clear the material is very well researched and referenced. AI is placed in context and difficulties such as ethical problems and bias are addressed as well as the exciting new science produced. The writing style is excellent, the abstracts for each chapter are useful, and the text is easy to read.” --Jeremy Frey, Professor of Physical Chemistry, University of Southampton, UK."This book is brilliant and contains loads of gems that will be invaluable to scientists and people working in AI."--Robert West, Professor Emeritus of Health Psychology, University College London.

• Preface. Acknowledgements. About the Author. 1 Introduction: AI and the Digital Revolution in Science. 2 AI for Managing Scientific Literature and Evidence. 3 AI for Data Interpretation. 4 AI for Reproducible Research. 5 Limitations of AI and Strategies for Combating Bias. 6 Conclusion: AI and the Future of Scientific Discovery. Index.

Janna Hastings is a computer scientist with more than a decade of experience across the life, behavioural and social sciences. She is also a data scientist with a PhD in computational biology and extensive experience in bioinformatics, cheminformatics and psychoinformatics. She is currently Assistant Professor of Medical Knowledge and Decision Support at the University of Zurich, and Vice-Director of the School of Medicine at the University of St. Gallen. Her current research focuses on bridging the gaps between knowledge and learning to bring AI technologies in medicine closer to the needs and workflows of clinicians and to support truly interdisciplinary and integrative knowledge discovery.

“An excellent summary of the state of the art of AI for Scientific Discovery. A concise and informative book covering the main areas of the topic. It is clear the material is very well researched and referenced. AI is placed in context and difficulties such as ethical problems and bias are addressed as well as the exciting new science produced. The writing style is excellent, the abstracts for each chapter are useful, and the text is easy to read.” --Jeremy Frey, Professor of Physical Chemistry, University of Southampton, UK."This book is brilliant and contains loads of gems that will be invaluable to scientists and people working in AI."--Robert West, Professor Emeritus of Health Psychology, University College London.

• Preface. Acknowledgements. About the Author. 1 Introduction: AI and the Digital Revolution in Science. 2 AI for Managing Scientific Literature and Evidence. 3 AI for Data Interpretation. 4 AI for Reproducible Research. 5 Limitations of AI and Strategies for Combating Bias. 6 Conclusion: AI and the Future of Scientific Discovery. Index.

Janna Hastings is a computer scientist with more than a decade of experience across the life, behavioural and social sciences. She is also a data scientist with a PhD in computational biology and extensive experience in bioinformatics, cheminformatics and psychoinformatics. She is currently Assistant Professor of Medical Knowledge and Decision Support at the University of Zurich, and Vice-Director of the School of Medicine at the University of St. Gallen. Her current research focuses on bridging the gaps between knowledge and learning to bring AI technologies in medicine closer to the needs and workflows of clinicians and to support truly interdisciplinary and integrative knowledge discovery.

“An excellent summary of the state of the art of AI for Scientific Discovery. A concise and informative book covering the main areas of the topic. It is clear the material is very well researched and referenced. AI is placed in context and difficulties such as ethical problems and bias are addressed as well as the exciting new science produced. The writing style is excellent, the abstracts for each chapter are useful, and the text is easy to read.” --Jeremy Frey, Professor of Physical Chemistry, University of Southampton, UK."This book is brilliant and contains loads of gems that will be invaluable to scientists and people working in AI."--Robert West, Professor Emeritus of Health Psychology, University College London.

• Preface. Acknowledgements. About the Author. 1 Introduction: AI and the Digital Revolution in Science. 2 AI for Managing Scientific Literature and Evidence. 3 AI for Data Interpretation. 4 AI for Reproducible Research. 5 Limitations of AI and Strategies for Combating Bias. 6 Conclusion: AI and the Future of Scientific Discovery. Index.