Article
Health Care Sciences & Services
Roman Zeleznik, Jakob Weiss, Jana Taron, Christian Guthier, Danielle S. Bitterman, Cindy Hancox, Benjamin H. Kann, Daniel W. Kim, Rinaa S. Punglia, Jeremy Bredfeldt, Borek Foldyna, Parastou Eslami, Michael T. Lu, Udo Hoffmann, Raymond Mak, Hugo J. W. L. Aerts
Summary: The study evaluated the use of a deep-learning system for heart segmentation on CT scans in radiation oncology treatment planning. The system, trained with multi-center data and validated in a real-world dataset, showed improved segmentation time and agreement compared to manual methods. The results indicate that deep-learning algorithms can be successfully applied across medical specialties to enhance clinical care.
NPJ DIGITAL MEDICINE
(2021)
Review
Public, Environmental & Occupational Health
David B. Olawade, Ojima J. Wada, Aanuoluwapo Clement David-Olawade, Edward Kunonga, Olawale Abaire, Jonathan Ling
Summary: Artificial intelligence (AI) is rapidly evolving and revolutionizing many aspects of healthcare. Its application in public health faces challenges such as limited infrastructure, data scarcity, and ethical concerns. However, it has also aided various aspects of public health delivery, such as risk prediction and disease forecasting.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Medical Informatics
Winston R. Liaw, John M. Westfall, Tyler S. Williamson, Yalda Jabbarpour, Andrew Bazemore
Summary: A recently released report from the National Academy of Medicine suggests that the use of artificial intelligence (AI) outside of hospitals, such as conversational agents, cameras, and remote sensors, has the potential to improve health. However, the success of AI is not guaranteed, and stakeholders need to be involved in its development to ensure easy usability by clinicians, protection of patient privacy, and enhanced value of care. Primary care, as the largest healthcare delivery platform, plays a crucial role in the adoption and potential exacerbation of health disparities. To fully leverage the benefits of AI, primary care needs to become a medical home for AI, expanding its teams and training, and capitalizing on government initiatives and funding.
JMIR MEDICAL INFORMATICS
(2022)
Review
Biochemistry & Molecular Biology
Peng-Chan Lin, Yi-Shan Tsai, Yu-Min Yeh, Meng-Ru Shen
Summary: This article discusses the use of artificial intelligence in precision cancer medicine, covering topics such as computational prediction, mutational analysis, single-cell genomics, and text mining. By utilizing AI medical platforms and visualization techniques, large amounts of clinical biodata can be processed and understood quickly, leading to more accurate and rapid cancer therapy targets.
Article
Management
Julian Senoner, Torbjorn Netland, Stefan Feuerriegel
Summary: The study developed a data-driven decision model leveraging explainable artificial intelligence to enhance process quality in manufacturing. By using nonlinear modeling with Shapley additive explanations, the model infers the relationship between production parameters and process quality, allowing prioritization of quality improvement processes by manufacturers. The decision model selects improvement actions based on quality management theory to target sources of quality variation.
MANAGEMENT SCIENCE
(2022)
Review
Multidisciplinary Sciences
Paulina Cecula, Jiakun Yu, Fatema Mustansir Dawoodbhoy, Jack Delaney, Joseph Tan, Iain Peacock, Benita Cox
Summary: This review summarizes findings in the fields of AI in psychiatry and patient flow from the past 5 years, identifies a research gap in patient flow studies, and suggests potential directions for future research.
Review
Computer Science, Information Systems
Meina Zhang, Linzee Zhu, Shih-Yin Lin, Keela Herr, Chih-Lin Chi, Ibrahim Demir, Karen Dunn Lopez, Nai-Ching Chi
Summary: This review investigates the current state of research on AI-based interventions for pain assessment and management in adult patients. The findings suggest that these interventions have a positive effect on pain recognition, prediction, and self-management. However, more research is needed before they can be applied in large-scale clinical trials.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2023)
Article
Computer Science, Information Systems
Na Man, Kechao Wang, Lin Liu
Summary: The study shows that using computer cognitive maps can significantly improve students' divergent thinking ability.
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
(2021)
Article
Medicine, General & Internal
Helena Silveira Schuch, Mariane Furtado, Gabriel Ferreira dos Santos Silva, Ichiro Kawachi, Alexandre D. P. Chiavegatto Filho, Hawazin W. Elani
Summary: This study used machine learning models to predict adults at risk of foregoing preventive dental care and found biases against underrepresented sociodemographic groups.
Article
Dermatology
Michelle Barakat-Johnson, Aaron Jones, Mitch Burger, Thomas Leong, Astrid Frotjold, Sue Randall, Judith Fethney, Fiona Coyer
Summary: This study evaluated the usability and effectiveness of an artificial intelligence application for wound assessment and management. The results showed that the intervention group, which used the application, had significantly improved wound documentation compared to the standard group. The use of the application facilitated remote patient monitoring and reduced patient travel time while maintaining optimal wound care.
INTERNATIONAL WOUND JOURNAL
(2022)
Article
Medicine, General & Internal
Ya-Huei Chen, Jia-Lang Xu
Summary: Falls are common adverse events in hospitalized patients, which can lead to physical and economic burdens. Therefore, predicting the risks of falls in hospitalized patients is important for providing effective fall prevention measures.
FRONTIERS IN MEDICINE
(2023)
Article
Health Care Sciences & Services
Glorin Sebastian, Amrita George, George Jackson
Summary: The aim of this study was to examine whether communication strategies (ethos, pathos, and logos) are effective in overcoming factors that hinder AI product adoption among patients. The results indicate that using communication strategies in promoting AI products can improve users' trust, customer innovativeness, and perceived novelty value, leading to increased adoption.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Health Care Sciences & Services
Matthias Klumpp, Marcus Hintze, Milla Immonen, Francisco Rodenas-Rigla, Francesco Pilati, Fernando Aparicio-Martinez, Dilay Celebi, Thomas Liebig, Mats Jirstrand, Oliver Urbann, Marja Hedman, Jukka A. Lipponen, Silvio Bicciato, Anda-Petronela Radan, Bernardo Valdivieso, Wolfgang Thronicke, Dimitrios Gunopulos, Ricard Delgado-Gonzalo
Summary: The paper presents a comparative approach from nine European hospitals and eleven different use cases to explore the application areas and benefits of hospital AI technologies. By examining these real-life cases, insights are provided for the practical application of AI technologies in healthcare.
Article
Optics
Bowei Dong, Samarth Aggarwal, Wen Zhou, Utku Emre Ali, Nikolaos Farmakidis, June Sang Lee, Yuhan He, Xuan Li, Dim-Lee Kwong, C. D. Wright, Wolfram H. P. Pernice, H. Bhaskaran
Summary: This paper introduces new developments in hardware-based accelerators, including electronic tensor cores and photonic implementations. By modulating photonic signals and combining them with distributed memories and wavelength multiplexing, we configure the system to be compatible with edge computing frameworks. Through processing electrocardiogram data and constructing a convolutional neural network, we demonstrate that this method can accurately identify patients at risk of sudden cardiac death.
Review
Medicine, General & Internal
Yaron Ilan
Summary: The legalization of cannabis products and the interest in their therapeutic benefits have created new opportunities for therapy and marketing. However, the variability in formulations, administration modes, therapeutic regimens, and patient responses make it difficult to standardize medical cannabis-based treatments. Digital medical cannabis, controlled by second-generation AI systems, aims to improve patient responses and address tolerance and variability issues. It offers potential benefits for standardization, therapeutic efficacy, cost reduction, and revenue increase.
FRONTIERS IN MEDICINE
(2022)
Article
Statistics & Probability
Skyler Speakman, Edward McFowland, Daniel B. Neill
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2015)
Article
Computer Science, Artificial Intelligence
Seth R. Flaxman, Daniel B. Neill, Alexander J. Smola
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2016)
Article
Mathematical & Computational Biology
Sriram Somanchi, Daniel B. Neill, Anil V. Parwani
STATISTICS IN MEDICINE
(2018)
Editorial Material
Computer Science, Artificial Intelligence
Daniel B. Neill
IEEE INTELLIGENT SYSTEMS
(2012)
Editorial Material
Computer Science, Artificial Intelligence
Christopher A. Harle, Daniel B. Neill, Rema Padman
IEEE INTELLIGENT SYSTEMS
(2012)
Article
Statistics & Probability
Daniel B. Neill
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2012)
Article
Mathematical & Computational Biology
Daniel B. Neill, Edward McFowland, Huanian Zheng
STATISTICS IN MEDICINE
(2013)
Article
Computer Science, Interdisciplinary Applications
Dylan Fitzpatrick, Yun Ni, Daniel B. Neill
Summary: The SVSS method accurately detects spatial clusters without imposing constraints on shape or size, enabling the discovery of practically useful anomalous patterns for disease surveillance, crime hotspot detection, and pothole cluster detection.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Statistics & Probability
Charles A. Pehlivanian, Daniel B. Neill
Summary: This article presents a generalization of spatial and subset scan statistics from the single to the multiple subset case. It discusses the definitions and properties of risk partitioning and multiple cluster detection scan statistics, and proposes an optimization method for efficient computation. Theoretical conditions for the optimality of consecutive partitions are proved, and a dynamic programming approach is introduced for large-scale risk partitioning and multiple cluster detection problems. The performance and practical utility of partition scan statistics are demonstrated using simulated and real-world data.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2023)
Article
Multidisciplinary Sciences
Mallory Nobles, Ramona Lall, Robert W. Mathes, Daniel B. Neill
Summary: Existing public health surveillance systems are effective at monitoring known illnesses, but there is a need for innovation in detecting rare biothreats. We introduce a data-driven, automated machine learning approach for presyndromic surveillance that learns emerging syndromes and provides personalized decision support.
Proceedings Paper
Computer Science, Artificial Intelligence
Feng Chen, Daniel B. Neill
PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14)
(2014)
Article
Automation & Control Systems
Edward McFowland, Skyler Speakman, Daniel B. Neill
JOURNAL OF MACHINE LEARNING RESEARCH
(2013)
Article
Social Work
Daniel B. Neill, William Herlands
JOURNAL OF TECHNOLOGY IN HUMAN SERVICES
(2018)
Article
Psychology, Multidisciplinary
Brad J. Bushman, Katherine Newman, Sandra L. Calvert, Geraldine Downey, Mark Dredze, Michael Gottfredson, Nina G. Jablonski, Ann S. Masten, Calvin Morrill, Daniel B. Neill, Daniel Romer, Daniel W. Webster
AMERICAN PSYCHOLOGIST
(2016)
