CURATE.AI – AI-derived personalized tacrolimus dosing for pediatric liver transplant: A retrospective study

Tan, Shi-Bei; Kumar, Kirthika Senthil; Gan, Tiffany Rui Xuan; Truong, Anh T. L.; Tan, Lester W. J.; Blasiak, Agata; Mali, Vidyadhar Padmakar; Aw, Marion; Ho, Dean

doi:10.1101/2022.11.24.22282708

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Accordingly, collaborative functioning together with awareness, evidence, patient safety and data were key aspects that de ned CURATE.AI adoption by doctors as gauged in a formal, behavioral study. 19 Usercentered design may contribute to realizing the potential of CURATE.AI and other CDSS and their adoption at-scale. A systematic review of higher level CDSS in oncology concluded that CDSS failed to improve clinical outcome.…”

Section: Discussionmentioning

confidence: 99%

Dynamic, personalized dose selection platform, CURATE.AI, in patients with solid tumors: case series and pilot feasibility trial

Sundar,

Blasiak,

Truong

et al. 2024

Preprint

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In oncology, the conventional reliance on maximum tolerated doses (MTD) strategy for chemotherapy may not optimize treatment outcomes for individual patients. Artificial intelligence (AI) holds promise to support treatment personalization strategies. In this study we present the experience of feasibility testing of CURATE.AI for individualized dose optimization of capecitabine in the treatment of 10 patients with advanced solid tumors at/for treatment with single-agent capecitabine, capecitabine in combination with oxaliplatin (XELOX), or capecitabine in combination with irinotecan (XELIRI) recruited under PRECISE CURATE.AI trial. CURATE.AI is an AI-derived platform that utilizes a patient’s own, small dataset to dynamically personalize only their own dose recommendations. This case series highlights the logistical and scientific feasibility of providing dynamically personalized AI-derived chemotherapy dose recommendations in the setting of a prospective clinical trial.

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Section: Discussionmentioning

confidence: 99%

Dynamic, personalized dose selection platform, CURATE.AI, in patients with solid tumors: case series and pilot feasibility trial

Sundar,

Blasiak,

Truong

et al. 2024

Preprint

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“…The small-scale experiments mentioned in earlier sections have paved the way for CURATE.AI-based retrospective studies over the years. [ 79 80 81 ] The retrospective studies serve to assess the feasibility of prospective trials for CURATE.AI, help formulate hypotheses, identify potential risk factors and assist in trial designs. Some of these studies have progressed to clinical trials for further validation [ Table 1 ].…”

Section: Curateai and N-of-1 Medicine: Use Casesmentioning

confidence: 99%

N-of-1 medicine

Wang,

Leong,

Lau

et al. 2024

Singapore Medical Journal

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The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient’s own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

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“…CURATE.AI has been clinically assessed across multiple indications, ranging from oncology to immunosuppression. These have included prospective, interventional studies, as well as retrospective analysis studies [8][9][10][11][12][13][14][15]. It has also been explored in the domain of personalized cognitive training in healthy individuals [16].…”

Section: Introductionmentioning

confidence: 99%

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

Vijayakumar,

Lee,

Leong

et al. 2023

JMIR Hum Factors

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Background Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians’ perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians’ perceptions and expectations toward their downstream implementation. This paper explores physicians’ perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)–based and clinical stage personalized dosing CDSSs, into clinical practice. Objective This study aims to understand physicians’ perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. Methods A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. Results Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. Conclusions The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians’ expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.

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CURATE.AI – AI-derived personalized tacrolimus dosing for pediatric liver transplant: A retrospective study

Cited by 4 publications

References 19 publications

Dynamic, personalized dose selection platform, CURATE.AI, in patients with solid tumors: case series and pilot feasibility trial

Dynamic, personalized dose selection platform, CURATE.AI, in patients with solid tumors: case series and pilot feasibility trial

N-of-1 medicine

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

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