European Doctors Express Cautious Optimism and Concerns About AI in Healthcare
While European doctors are cautiously optimistic about AI's potential to improve healthcare, significant concerns remain regarding knowledge gaps, ethical implications, and the need for robust regulation and oversight.
캘리포니아주, 건강 보험사의 AI 사용 제한하는 법안 통과
캘리포니아주는 의료 분야에서 AI 사용에 대한 규제를 강화하여 의료 결정에서 인간의 역할을 보호하고 알고리즘 편향 가능성을 해결하고자 합니다.
The Urgent Need for Human-Centered Design in AI-Enabled Healthcare to Prioritize Care
To realize the full potential of AI in healthcare and avoid exacerbating existing issues, a paradigm shift is needed, prioritizing human-centered design and focusing on augmenting care within healthcare systems.
TrialMind: An AI Pipeline for Enhanced Clinical Evidence Synthesis Using Large Language Models
TrialMind, an AI-driven pipeline, leverages large language models to significantly accelerate and improve the process of clinical evidence synthesis by streamlining study search, screening, and data extraction, ultimately enabling more efficient and accurate updates to clinical practice guidelines and drug development.
고관절 골절 환자 경로 및 에이전트 기반 모델링: 아일랜드 병원의 과밀 문제 해결을 위한 인공지능 및 머신러닝 활용
본 논문은 아일랜드 병원의 과밀 문제를 해결하기 위해 고관절 골절 환자의 경로 최적화에 인공지능(AI) 및 머신러닝(ML) 기술을 적용하는 방법을 제시합니다.
Leveraging Machine Learning and Agent-Based Modeling to Optimize Hip Fracture Patient Flow in Irish Hospitals
This research explores the potential of machine learning and agent-based modeling to address overcrowding in Irish hospitals by optimizing patient flow, specifically focusing on hip fracture patients as a case study.
The Current and Future Impact of AI in Healthcare: Insights from an Expert Panel Discussion
While AI is already demonstrating its potential in healthcare, particularly in pathology and drug development, its full impact is yet to be realized, requiring careful navigation and education to harness its transformative power for patient care.
AI-Powered Predictive Analytics for Improved Patient Outcomes in the ICU
AI-powered predictive analytics in the ICU leverage real-time data analysis to predict and prevent complications, enabling early intervention and improving patient outcomes.
AI's Impact on Patient Care: How AI Tools Reduced Unexpected Deaths in Toronto
AI-driven clinical decision support tools, particularly those developed locally with clinician involvement, can significantly improve patient outcomes, as demonstrated by a case in Toronto where these tools led to a reduction in unexpected deaths.
AI in Clinical Decision Support: Potential and Challenges for Improved Patient Care
AI has the potential to revolutionize clinical decision support and predictive analytics, leading to better patient outcomes, but addressing bias in AI models is crucial for responsible implementation.
