The pharma industry continues to experiment with AI to see where it can be implemented to improve results and simplify workflows. Although many researchers in the industry have been working with algorithms and machine-learning technology for several years now, the recent expansion in AI tech from the public is pushing the industry to test the technology in any possible use case.
How is Pharma experimenting with AI?
Key Takeaways
- Pharma continues to experiment with the use of AI in unique use cases across the industry.
- Arkstone's study suggests that AI can produce faster therapeutic recommendations than traditional methods.
- The technology also appears to provide accurate detection of pathogen resistance.
Arkstone Medical Solutions published a peer-reviewed study in Life that tested the use of AI and machine-learning in treating bloodstream infections, particularly in settings with a high risk of antimicrobial resistance.1 The study, titled “AI-Based Treatment Recommendations Enhance Speed and Accuracy in Bacteremia Management,” was conducted in Lima, Peru, and included 117 patients.
According to Akstone, the study showed that the technology could provide accurate therapeutics recommendations 29 hours faster than traditional methods. There was also a significant increase in pathogen and resistance detection.
In a press release, Arkstone’s chief science officer and study co-author Dr. Ari Frenkel said, “This research marks a major milestone for Arkstone's mission: to deliver faster, more precise, and more accessible antimicrobial stewardship through clinical decision support. With infectious diseases like sepsis, time is of the essence. Getting effective treatment started even one day earlier, let alone 29 hours earlier, can dramatically change patient outcomes."
In the same press release, Dr. Juan C. Gomez de la Torre, director of molecular informatics and clinical research at Arkstone and medical director at Roe Laboratory, added, “This is among the first studies to rigorously assess the reliability of molecular diagnostics integrated with AI-driven CDSS compared to traditional phenotypic testing. Our findings affirm that molecular diagnostics, when combined with robust antimicrobial stewardship, offer timely, precise guidance without compromising clinical accuracy."