AI am the future: artificial intelligence in pediatric rheumatology DOI
Saverio La Bella, Latika Gupta, Vincenzo Venerito

et al.

Current Opinion in Rheumatology, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

Purpose of review There is a growing interest in the applications artificial intelligence pediatric rheumatology. Although concerns with training datasets, ethical considerations, and need for major utilization explainable are still ongoing challenges, significant advancements have been made recent years. In this review, we explore most rheumatology, special focus on machine learning models their outcomes. Recent findings Supervised unsupervised largely employed to identify key biomarkers, predict treatment responses, stratify patients based disease presentation progression. addition, innovative driven imaging tools noninvasive diagnostic methods improved accuracy emerged as encouraging solutions identifying inflammation activity. Large language utilized patient-based questions promising results. Nevertheless, critical examination human oversight crucial interpreting intelligence's outputs. Summary Artificial revolutionizing rheumatology by improving diagnosis classification, patient stratification personalized treatment. However, only at beginning, adventure has just begun.

Language: Английский

A generalist medical language model for disease diagnosis assistance DOI
Xiaohong Liu, Hao Liu, Guoxing Yang

et al.

Nature Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Language: Английский

Citations

3
Enhancement and assessment in the AI age: An extended mind perspective DOI Creative Commons
José Hernández‐Orallo

Journal of Pacific Rim Psychology, Journal Year: 2025, Volume and Issue: 19

Published: Jan. 1, 2025

On the verge of AI Age—the Mechanocene—we tend to reproduce past narratives where humans are replaced by machines. This leads us identifying skills and activities that will not be automated soon, prepare both current future generations for those ‘safe’ occupations. However, there is risk set non-automatable tasks becomes empty sooner than expected, preparing new needed in end. Instead this incremental route, paper we imagine a final destination has all have. We argue an age can intensively assisted, augmented coupled with AI, need rethink enhancement assessment under extended mind thesis—a philosophical theory suggesting technological tools become integral parts our cognitive processes. Under perspective, still identify useful age, especially if want understand influence their world. Then, these as goals education, reliably assess they achieved. world ubiquitous extenders generates enormous challenges individuals, when mostly operating part AI-human hybrids collectives. In context, individual, human or machine, must evaluated terms contribution human-machine teams expected embedded in. further takes education from traditional aspiration achieving fully autonomous reality more integrated interdependent scenarios age.

Language: Английский

Citations

0
Reinventing instructional laboratory with ChatGPT: Radiation measurement by smartphone DOI
Chitnarong Sirisathitkul, Yaowarat Sirisathitkul

Innovations in Education and Teaching International, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Feb. 14, 2025

Language: Английский

Citations

0
AI am the future: artificial intelligence in pediatric rheumatology DOI
Saverio La Bella, Latika Gupta, Vincenzo Venerito

et al.

Current Opinion in Rheumatology, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

Purpose of review There is a growing interest in the applications artificial intelligence pediatric rheumatology. Although concerns with training datasets, ethical considerations, and need for major utilization explainable are still ongoing challenges, significant advancements have been made recent years. In this review, we explore most rheumatology, special focus on machine learning models their outcomes. Recent findings Supervised unsupervised largely employed to identify key biomarkers, predict treatment responses, stratify patients based disease presentation progression. addition, innovative driven imaging tools noninvasive diagnostic methods improved accuracy emerged as encouraging solutions identifying inflammation activity. Large language utilized patient-based questions promising results. Nevertheless, critical examination human oversight crucial interpreting intelligence's outputs. Summary Artificial revolutionizing rheumatology by improving diagnosis classification, patient stratification personalized treatment. However, only at beginning, adventure has just begun.

Language: Английский

Citations

0