Guidelines are statements that include recommendations intended to optimize patient care that are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options11. Whether in discussions with colleagues during multidisciplinary team meetings or conducting one-on-one consultations with patients, physicians are expected to implement this data driven best practice. This challenge becomes even more pronounced in the context of low-volume centers, rare cancer entities, and in trainees who oncologic knowledge may be incomplete. Written guidelines play a central role in this process, but disease stage specific information is often spread in hundreds of pages of text and time is a limiting factor to check every decision for its guideline adherence.On average, physicians can only spend 4.6 h a week to acquire the most recent information, leading to an increasing gap between clinically applied treatment and actually available evidence-based recommendations12,13,14.Given the excessive amount of data generated during the treatment of prostate cancer patients, a CDSS is a valuable tool for extracting and presenting relevant information in a time-efficient manner9. Automated AI supported data interpretation and decision-making are next milestones in AI-assisted patient care.Prior applications have focused on early detection and treatment specific automated calculation and processing. Even the advanced approach with IBM`s Watson for Oncology focused mainly on therapeutic options while AIPC not only suggests therapeutic but also diagnostic options6,13.This study represents the initial exploration of APIC’s advanced feature aimed at providing automated guideline-conforming recommendations for clinical decision-making in patients with prostate cancer. Within the protected framework of fictional patients our study is a first step on this journey.The result of this study demonstrate that the use of guidelines is very time consuming. Prior studies already demonstrated that the application of the AIPC software has resulted in a noteworthy impact on time expenditure for case preparation9.The application of the decisions support tool led to a highly significant reduction of overall time (-94%) required for the entire clinical decision-making process as compared to conventional approach with the usage of written guidelines. This time saving effect is of particular value as it enables the treating physicians to allocate their precious and already limited time in clinical practice not to searching processes but to focusing more on the patient’s individual treatment.Here, we also demonstrate the potential of CDSS to improve the quality of decision making. By using the software, the rate of incomplete and wrong decisions significantly decreased from 33 to 0% and incomplete decisions decreased from 39 to 0% as compared to the first setting using no guidelines.These results highlight the potential of CDSS to augment our adherence to established clinical guidelines, ensuring that our medical interventions align with the latest evidence-based practices. By doing so, it not only contributes to better patient outcomes but also underscores our commitment to providing the highest quality of care while optimizing resource allocation15,16,17. Especially out of the patient’s perspective establishing extra safety barriers in order to prevent medical errors is of prime interest18,19.The utilization of the software enables clinicians to operate automatically at the cutting edge of oncologic knowledge including yearly updates.As a result, modifications in guideline recommendations could become more promptly evident, given its integration into clinical routines. This fosters a continuous educational effect, particularly through the software’s capability to link specific recommendations with corresponding text passages in the EAU Guidelines document.Despite the multicenter setting and the overall analysis of 300 cases, this study is associated with limitations. First, “only” 10 resident urologists solved “only” 10 clinical cases each, thus limiting the diversity of settings. Secondly, these clinical cases were preconstructed but nonetheless reflect routine cases present in everyday clinical practice. Lastly, since the clinical experience of our participants ranges between 1 and 6 years, the generalization to the full spectrum of urologists is limited Nonetheless, even among experienced clinicians, non-adherence to oncologic guidelines continues to be a persistent challenge and AI-supported tools may offer a valuable solution8,20,21.Prostate cancer as a highly variable medical condition, demands clinicians to carefully weigh clinical advantages, patient life expectancy, concurrent health issues, and potential treatment-related complications when making decisions. Therefore, a software might support but not replace a human made clinical decision to fully implement all important individual factors.Importantly, AIPC also allows for individualized options and therapy interruptions, ensuring that the user is not coerced into a specific decision but instead maintains flexibility.Overall, the utilization of AIPC resulted in a noteworthy reduction in the time required for making verified clinical decisions, indicating increased efficiency. Moreover, the software demonstrated a significantly stronger adherence to clinical guidelines and improved quality of decision-making.As a future outlook, the application of CDSS has wide implications20,21 not only for prostate cancer treatment but multiple tumor entities as well as benign conditions and can help to streamline and optimize the patient pathway and treatment quality. Especially in cases involving more advanced tumor stages, where treatment options become increasingly complex, software-based support, such as AIPC, hold promising potential.
