AI and HealthCare, Author Interviews, Cannabis, Pharmacology, Technology / 28.08.2025

[caption id="attachment_70470" align="alignleft" width="150"]Duncan Dobbins, PharmD, MHIGeisinger College of Health Sciences Scranton, Pennsylvania Dr. Dobbins, PharmD[/caption] MedicalResearch.com Interview with: Duncan Dobbins, PharmD, MHI Geisinger College of Health Sciences Scranton, Pennsylvania MedicalResearch.com: What prompted this commentary, and what did you find? Response: In theory, there could be a drug interaction between immunotherapy and medical cannabis. A small (N=102) observational report from Israel appeared to find that immunotherapies worked much less well in cancer patients who also used medical cannabis.1 However, a follow up report2 took about two weeks and involved manually rechecking the math and data-analysis. Several discrepancies emerged between the methods and results. Two-tailed tests were listed in the methods yet one-tailed p values appeared in the results. Arithmetic errors, some traceable to unconventional “floor” rounding, affected key percentages. Multiple p values in Table 1 (21 out of 22) could not be reproduced with the stated tests. Finally, smoking status, a key confound, was not reported. Taken together, these issues complicate interpretation and highlight how small computational slips can cascade into larger inferential uncertainty. For this follow-up report, I was asked, “Do you think AI could have double checked this math?”
AI and HealthCare / 27.06.2025

[caption id="attachment_69270" align="aligncenter" width="500"]artificial-intelligence-in-healthcare Photo by ThisIsEngineering[/caption] Over the past decade, artificial intelligence (AI) and machine learning (ML) have been hailed as game-changers across multiple industries, and healthcare is no exception. From diagnostic imaging to personalized treatments, AI is transforming how we understand and treat disease. Among the most promising areas is clinical research—where AI and ML are touted as tools to make trials faster, smarter, and more efficient. But as the buzz around these technologies grows, so does skepticism. Are we really witnessing a revolution in clinical trials, or is much of the talk around AI still more hype than reality?

The Promises of AI in Clinical Research

AI’s application in clinical trials spans a wide array of use cases. One of the biggest promises lies in patient recruitment and matching. Traditional recruitment methods often lead to delays, with over 80% of trials failing to meet enrollment timelines. AI, through natural language processing (NLP) and predictive modeling, can scan electronic health records (EHRs) and other datasets to identify eligible participants with remarkable speed and accuracy. Beyond recruitment, AI is being used to optimize protocol design, predict patient dropout rates, monitor adverse events in real-time, and even simulate synthetic control arms to reduce placebo usage. Machine learning algorithms can also mine historical trial data to detect patterns or predict success probabilities, potentially saving millions in drug development costs.