📢 AI and ML in clinical trials: A 14-year perspective
💡 Takeaway:
AI/ML researchers and stakeholders must prioritize quality evidence, equitable study distribution, and transparent result reporting to maximize clinical impact and minimize
🔗 Read more: https://tnyp.me/jG1jMhnS/m
AI/ML ClinicalTrials (2010-2023)
📈 3106 studies; rapid growth since 2017.
📊 7.6% FDA-regulated.
🔍 56.2% randomized, 58.9% prospective.
🏥 44.2% hospital-backed, 28% academic.
🌍 75.3% in high-income countries.
#AIML #ClinicalResearch #Trends #HIC https://tnyp.me/jG1jMhnS/m
Title: AI/ML Clinical Trial Trends, 2010-2023
🔍 7.6% regulated by FDA
📊 54.5% ongoing; 27.1% completed
🌐 75.3% in high-income countries
🔄 62.8% started post-2021
Trade-offs in Medical AI
• 🤔 Private funding complicates patient-industry benefits.
• 🧑⚕️ Need patient-physician participation.
• 📊 Clinically relevant metrics lacking.
💡 Takeaway:
Researchers and policymakers should aim to address geographic and transparency disparities in AI/ML clinical research to enhance its global applicability and reliability.
🔗 Read more: https://tnyp.me/jG1jMhnS/m
🔍 How NumPy Shapes Scientific Discovery!
🔗 Interoperability - Serves as a bridge for various computation libraries
🔧 Flexibility - Supports a decade of future analyses
#NumPy #ArrayProgramming #Science https://tnyp.me/ih07Sizm/m
New derivative, PEGyAMPH, offers anti-obesity effects without cardiovascular impacts.
🔬 Unlike AMPH, no rise in blood pressure or heart rate.
📊 Enhances insulin sensitivity despite glucose intolerance.
#ObesityResearch #Amphetamines #MetabolicDisorders
https://tnyp.me/kzRoO838/m
Array programming with NumPy is essential to scientific computing.
📚 Ubiquitous in education across scientific fields.
#NumPy #Python #SciPy #Matplotlib #GPU #TPU
https://tnyp.me/ih07Sizm/m
