Results 31 to 40 of about 2,986,386 (301)

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Intricacies of human–AI interaction in dynamic decision-making for precision oncology

Nature Communications
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals’ cancer progression for effective personalized care.
Dipesh Niraula, Kyle C. Cuneo, Ivo D. Dinov, Brian D. Gonzalez, Jamalina B. Jamaluddin, Jionghua Judy Jin, Yi Luo, Martha M. Matuszak, Randall K. Ten Haken, Alex K. Bryant, Thomas J. Dilling, Michael P. Dykstra, Jessica M. Frakes, Casey L. Liveringhouse, Sean R. Miller, Matthew N. Mills, Russell F. Palm, Samuel N. Regan, Anupam Rishi, Javier F. Torres-Roca, Hsiang-Hsuan Michael Yu, Issam El Naqa   +21 more
doaj   +1 more source

Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management

Life
Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance.
Juan Carlos Gómez de la Torre, Ari Frenkel, Carlos Chavez-Lencinas, Alicia Rendon, Yoshie Higuchi, Jose M. Vela-Ruiz, Jacob Calpey, Ryan Beaton, Isaac Elijah, Inbal Shachar, Everett Kim, Sofia Valencia Osorio, Jason James Lee, Gabrielle Grogan, Jessica Siegel, Stephanie Allman, Miguel Hueda-Zavaleta   +16 more
doaj   +1 more source

Bias and Inaccuracy in AI Chatbot Ophthalmologist Recommendations

Cureus, 2023
To evaluate the accuracy and bias of ophthalmologist recommendations made by three AI chatbots, namely ChatGPT 3.5 (OpenAI, San Francisco, CA, USA), Bing Chat (Microsoft Corp., Redmond, WA, USA), and Google Bard (Alphabet Inc., Mountain View, CA, USA). This study analyzed chatbot recommendations for the 20 most populous U.S.
Oca, Michael C, Meller, Leo, Wilson, Katherine, Parikh, Alomi O, McCoy, Allison, Chang, Jessica, Sudharshan, Rasika, Gupta, Shreya, Zhang-Nunes, Sandy   +8 more
openaire   +2 more sources

Recommendations: Enhancing AI Literacy for the United States of America

, 2023
This report contains recommendations for enhancing AI literacy for the United States of America. Recommendations include prompting the Biden administration to develop a campaign around AI literacy and invest in formal and informal education to advance ...
National Artificial Intelligence Initiative Office. The National AI Advisory Committee
core  

Circulating microRNA signatures of cachexia and cancer in Canis familiaris as a comparative oncology model for human disease

Molecular Oncology, EarlyView.
Circulating microRNAs as biomarkers of cachexia and sex‐specific cancer in senior dogs. In 25 client‐owned dogs, four circulating miRNAs (miR‐15a, miR‐15b, miR‐16, miR‐140) were downregulated in cachexia, with miR‐16 the strongest individual biomarker (AUC = 0.899).
Soon‐Seok Park, Kyongman An, Gyeonghwa Kim, Soo‐Nyun Choi, Hong‐Ki Lee, Keun Hur, Kyu‐Shik Jeong   +6 more
wiley   +1 more source

Data Fit for Health Equity: Learning Health Systems, AI, and the STANDING Together Recommendations

Learning Health Systems
Introduction Artificial Intelligence (AI) tools may deliver significant improvements in healthcare and Learning Health Systems are well positioned to benefit.
Elinor Laws, Neil Cockburn
doaj   +1 more source

From Word-of-Mouth to Word-of-Machine: Examining AI-Generated Travel Recommendations Through the Word-of-Machine (WOMa) Perspective

Tourism and Hospitality
Generative artificial intelligence (AI) is increasingly used in travel planning, yet limited research has examined how travelers evaluate AI-generated travel recommendations as an information source.
Cathrine Linnes, Giulio Ronzoni, Jerome Agrusa, Babu George, Joseph Lema   +4 more
doaj   +1 more source

Targeting the EpCAM‐AXL axis to overcome drug resistance in lung cancer

Molecular Oncology, EarlyView.
Lung cancer cells often evade therapy by hijacking signaling pathways. We reveal that cleaved EpCAM (sEpCAM) stabilizes the oncogenic protein AXL, driving NF‐κB and STAT3‐mediated chemoresistance. This EpCAM‐AXL axis identifies a high‐risk patient subset with poor prognosis.
Alexa Guerrero‐Alba, Mark Shacker, Timothy P. Fleming, Michael A. Smith, Ross M. Bremner, Narendra V. Sankpal   +5 more
wiley   +1 more source

Ethical considerations in AI for child health and recommendations for child-centered medical AI

npj Digital Medicine
There does not exist any previous comprehensive review on AI ethics in child health or any guidelines for management, unlike in adult medicine. This review describes ethical principles in AI for child health and provides recommendations for child ...
Seo Yi Chng, Mark Jun Wen Tern, Yung Seng Lee, Lionel Tim-Ee Cheng, Jeevesh Kapur, Johan Gunnar Eriksson, Yap Seng Chong, Julian Savulescu   +7 more
doaj   +1 more source