A constrained disorder principle-based second-generation artificial intelligence digital medical cannabis system: A...

Area of Science:

• Medical cannabis research
• Artificial intelligence in healthcare
• Chronic pain management

Background:

• Adherence to medical cannabis treatment plans is challenging for patients.
• The constrained-disorder principle (CDP) utilizes personalized variability signatures.
• Second-generation AI systems based on CDP can enhance chronic medication response.

Purpose of the Study:

• To analyze real-world data on chronic pain patients using second-generation AI.
• To assess the impact of AI on medical cannabis adherence and effectiveness.
• To evaluate personalized AI regimens for optimizing treatment outcomes.

Main Methods:

• Retrospective analysis of real-world data from 27 chronic pain patients.
• Utilized a CDP-based second-generation AI app (Altus Care™) for personalized dosing and administration.
• Included 16 patients with over one week of follow-up, assessing adherence and pain scores.

Main Results:

• AI-based personalized regimens demonstrated high engagement and adherence rates.
• 50% of patients exhibited high compliance with the AI-guided treatment plan.
• Patients who reported pain scores showed improvement in chronic pain management.

Conclusions:

• Preliminary data suggest AI-driven approaches can improve medical cannabis adherence and clinical effectiveness.
• Algorithm-based AI systems show potential for enhancing patient outcomes in chronic pain.
• Further confirmation through prospective controlled studies is warranted.