Role of Key Stakeholders with Focus on the Pharmaceutical Sector
AI in Diagnostics
The medical diagnostic process is complex and prone to errors, influenced by clinician expertise, time constraints, and collaboration. Artificial intelligence advancements, including supervised, unsupervised, and DL algorithms, improve diagnostics by processing large datasets efficiently. Supervised learning uses labeled data for predictions, unsupervised learning finds patterns without labels, and deep learning addresses complex data but has interpretability issues. MALDI-TOF MS with ClinProTools software enables rapid and accurate identification of two S. aureus sub-species, achieving 100% accuracy through a genetic analysis and a quick classifier model [133].
The AMRx diagnostic tool, an advanced AI/ML solution that predicts AMR with high sensitivity and specificity in real time, has the potential of revolutionizing infectious disease diagnostics without traditional culture methods (https://sciinv.org/).
