Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis

Artificial intelligence in colorectal multidisciplinary team meetings. What are the medicolegal implications?

AIM

To give an insight into areas for future development and suggestions in the complexities of incorporation of AI into human colorectal cancer (CRC) care while bringing into focus the importance of clinicians' roles in patient care.

METHODS

Existing literature around AI use in CRC care is reviewed and potential regulatory issues and medicolegal implications around its implementation in CRC multidisciplinary team meetings (MDTs) are identified.

RESULTS

Challenges with patient privacy and confidentiality, patient consent, inequity and bias, patient autonomy, as well as AI system transparency and the liability and accountability issues arising from complications that arise from AI-aided clinical decisions are important focusses associated with the use of AI in CRC MDTs.

CONCLUSION

Consideration of various medicolegal aspects of the use of AI in CRC MDTs is warranted to ensure its safe and smooth incorporation into CRC MDTs. AI function as a clinical decision support system and does not replace professional expertise.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review

OBJECTIVE

Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures.

MATERIALS AND METHODS

We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software.

RESULTS

Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation.

DISCUSSION AND CONCLUSION

AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.

Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review

Generating guideline-based recommendations during multidisciplinary team (MDT) meetings in solid cancers is getting more complex due to increasing amount of information needed to follow the guidelines. Usage of clinical decision support systems (CDSSs) can simplify and optimize decision-making. However, CDSS implementation is lagging behind. Therefore, we aim to compose a CDSS implementation model. By performing a scoping review of the currently reported CDSSs for MDT decision-making we determined 102 barriers and 86 facilitators for CDSS implementation out of 44 papers describing 20 different CDSSs. The most frequently reported barriers and facilitators for CDSS implementation supporting MDT decision-making concerned CDSS maintenance (e.g. incorporating guideline updates), validity of recommendations and interoperability with electronic health records. Based on the identified barriers and facilitators, we composed a CDSS implementation model describing clinical utility, analytic validity and clinical validity to guide CDSS integration more successfully in the clinical workflow to support MDTs in the future.