Embracing AI for evidence-based clinical decisions

Artificial intelligence (AI) technologies are not new to the clinical setting. However, the burgeoning inclusion of generative AI has started to make an increased impact in the industry and pique the interest of forward-thinking clinicians.

Although there’s strong promise with AI technologies in healthcare in a variety of applications, using AI for evidence-based decision making can help with pressing issues that clinicians face. With medical knowledge doubling every 73 days¹, clinicians struggle to keep up with the overwhelming amount of medical information that is being published, making it more difficult and time consuming to find resources to support their treatment decisions. Additionally, patient conditions are more complex due in part to an aging population and a growing number of patients with multiple comorbidities. Looking up potential conditions, factoring in these complexities, from multiple sources on top of an already busy workload takes time that many clinicians don’t have. AI technologies have been seen as a way to increase efficiency and are weaving their way into the clinical workflow process, but they have yet to effectively solve the problems clinicians face when it comes to decision making. With the recent launch of ClinicalKey AI 새 탭/창에서 열기, clinicians who have used the product were able to interact with the conversational search engine to find answers to their pressing clinical questions. Elsevier conducted a survey and interviews² with these users who gave feedback on the usability, responses, and time savings that were offered from ClinicalKey AI. 새 탭/창에서 열기

¹ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116346/ ² Interview included 30 participants, interviews included 5 participants