Article
Computer Science, Artificial Intelligence
Kerstin N. Vokinger, Urs Gasser
Summary: Regulatory frameworks for artificial intelligence are being developed on both sides of the Atlantic, eagerly anticipated by the scientific and industrial community. Commonalities and differences in approaches to AI in medicine are beginning to emerge.
NATURE MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Fernando Martinez-Plumed, Pablo Barredo, Sean O. Heigeartaigh, Jose Hernandez-Orallo
Summary: Experimental benchmarks like ImageNet and Atari games are crucial for advancing AI research. An analysis of results and papers linked to 25 popular benchmarks reveals that competition and collaboration dynamics in AI research are still not well understood. The study provides an innovative methodology to explore the behavior of different entrants in challenges, from academia to tech giants, in response to achievements.
NATURE MACHINE INTELLIGENCE
(2021)
Article
Environmental Sciences
Francesca Larosa, Sergio Hoyas, Javier Garcia-Martinez, J. Alberto Conejero, Francesco Fuso Nerini, Ricardo Vinuesa
Summary: Large language models provide an opportunity to advance climate and sustainability research. We believe that regulating and validating generative artificial intelligence models would benefit society more than stopping development.
NATURE CLIMATE CHANGE
(2023)
Editorial Material
Biochemistry & Molecular Biology
Zachi I. Attia, Paul A. Friedman
Summary: By applying artificial intelligence to electrocardiograms recorded by patients using Apple watches, we conducted a prospective, digital, remote study to enable large-scale screening for left ventricular dysfunction, a serious and under-detected cardiac disease. The study found that patients engaged with the system and that the watch electrocardiograms effectively screened for the disease.
Article
Public, Environmental & Occupational Health
Sandra Gillner
Summary: Despite high expectations, the extensive and rapid adoption of AI in medical diagnostics has not been realized. This study investigates the perception and navigation of AI providers in complex healthcare systems, revealing their self-organization to increase adaptability and the practices utilized to mitigate tensions within the healthcare subsystems.
SOCIAL SCIENCE & MEDICINE
(2024)
Editorial Material
Health Care Sciences & Services
Mirja Mittermaier, Marium M. Raza, Joseph C. Kvedar
Summary: Artificial intelligence is increasingly used in healthcare, particularly in surgery. While it holds promise in predicting outcomes and guiding surgeons, AI systems can also be biased, exacerbating existing inequalities. This impacts disadvantaged populations, who may receive less accurate algorithmic predictions or underestimate their need for care. Detecting and mitigating bias is crucial for creating fair and generalizable AI technology. This article discusses a recent study that developed a new strategy to address bias in surgical AI systems.
NPJ DIGITAL MEDICINE
(2023)
Article
Environmental Sciences
Victor O. K. Li, Jacqueline C. K. Lam, Jiahuan Cui
Summary: This article discusses the role and challenges of AI and big data technologies in environmental decision-making, raises a series of important questions, and summarizes the significance and innovation of the articles included in the special issue. It also highlights the important principles of AI for social good.
ENVIRONMENTAL SCIENCE & POLICY
(2021)
Article
Computer Science, Artificial Intelligence
Pat Pataranutaporn, Ruby Liu, Ed Finn, Pattie Maes
Summary: This study explores how changes in a person's mental model of an AI system affect their interaction with the system. It shows that perceiving a caring motive for the AI leads to a perception of greater trustworthiness, empathy, and performance. Initial mental models and priming have a stronger effect on more sophisticated AI models. The research also suggests a feedback loop between users and AI that reinforces the user's mental model over time. Further investigation is needed to understand the long-term effects.
NATURE MACHINE INTELLIGENCE
(2023)
Editorial Material
Biochemistry & Molecular Biology
Eric Wu, Kevin Wu, Roxana Daneshjou, David Ouyang, Daniel E. Ho, James Zou
Summary: A comprehensive overview of medical AI devices approved by the US Food and Drug Administration sheds light on limitations of the evaluation process that may mask vulnerabilities of devices when deployed on patients.
Article
Engineering, Industrial
Xin Hu, Ang Liu, Xiaopeng Li, Yun Dai, Masayuki Nakao
Summary: AI can improve customer segmentation in product development, but the lack of transparency often leads designers to doubt its predictions. Explainable AI (XAI) is a new paradigm that provides humanly understandable explanations about AI predictions. The use of XAI explanations, based on features and data, can enhance AI performance and foster trust in AI among designers. A new framework is proposed and validated through an experiment, showing that XAI can enhance AI performance by facilitating feature selection and identifying high-value datasets.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Michael H. Bernstein, Michael K. Atalay, Elizabeth H. Dibble, Aaron W. P. Maxwell, Adib R. Karam, Saurabh Agarwal, Robert C. Ward, Terrance T. Healey, Grayson L. Baird
Summary: This study aimed to investigate the impact of incorrect AI results on radiologist performance and explore the possibility of reducing errors through optimizing human factors. The results showed that incorrect AI results increased the number of errors in follow-up decisions made by radiologists compared to when they did not have AI assistance. However, this effect was mitigated when radiologists believed the AI results would be deleted or when a box was provided around the region of interest.
EUROPEAN RADIOLOGY
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Piero P. P. Bonissone, Alessandro Liani
Summary: IEEE CAI 2023 is the first conference with an industry focus, covering six verticals including Industrial AI, AI in Healthcare/Life Science, Transportation/Aerospace, Energy, Earth System Decision Support, and Social Implications of AI/Privacy.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2023)
Article
Biochemistry & Molecular Biology
Norman E. Sharpless, Anthony R. Kerlavage
Summary: Artificial intelligence, machine learning, and deep learning have diverse applications in cancer research and clinical care, and the National Cancer Institute (NCI) is actively involved in supporting and advancing these technologies. In addition to developing and evaluating AI tools, NCI focuses on fostering a culture of data sharing, training the next generation of scientists, promoting interdisciplinary collaborations, and ensuring ethical principles in AI research and technologies.
BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER
(2021)
Article
Computer Science, Information Systems
Joachim Roski, Ezekiel J. Maier, Kevin Vigilante, Elizabeth A. Kane, Michael E. Matheny
Summary: Artificial intelligence plays a critical role in deriving value from health and healthcare data, but there is a risk of another AI Winter due to decreased trust in AI solutions. By promoting self-governance and defining standards to mitigate risks, a more comprehensive approach to governing AI solutions can be achieved, filling gaps in existing legislation and regulations. Adherence to these standards, verified through certification/accreditation, could help prevent another AI Winter by maintaining trust in AI practices.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Article
Health Care Sciences & Services
Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Narrendar Ravichandran, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu
Summary: Artificial intelligence has shown its ability to extract insights from data, but ensuring fairness in high-stakes fields such as healthcare remains a concern. The notion of fairness in clinical contexts requires careful examination and alignment with ethical considerations. A multidisciplinary approach involving AI researchers, clinicians, and ethicists is necessary to bridge the gap between technical developments and clinical needs.
NPJ DIGITAL MEDICINE
(2023)
