The case for AI-driven cancer clinical trials – The efficacy arm in silico

Kolla, Likhitha; Gruber, Fred K.; Khalid, Omar; Hill, Colin; Parikh, Ravi B.

doi:10.1016/j.bbcan.2021.188572

Cited by 17 publications

(20 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The crux of IST design is the ability to recreate human physiology and pathology based on genetics and environment. Recently, in-silico modeling has been used to predict responses and identify patients that may benefit from immunotherapies to improve clinical trial design 49,56 . A DT designed for clinical trials would be augmented with more onboard analytical and intelligent functions to make adaptive decisions and suggestions to physicians to mimic intelligent human responses.…”

Section: Dts For Surgical Planningmentioning

confidence: 99%

Digital twins for health: a scoping review

Katsoulakis,

Wang,

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

show abstract

Section: Dts For Surgical Planningmentioning

confidence: 99%

Digital twins for health: a scoping review

Katsoulakis,

Wang,

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…To this end, virtual or in silico clinical trials (ISTs) have been proposed. ISTs assess the initial viability and potential of a technology, functioning as a preparatory step for RCTs [ 19 – 21 ]. The main difference is that, instead of human subjects, digital data is used.…”

Section: Radar-1 Through Radar-5: the Assessment Of Clinical Valuementioning

confidence: 99%

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice

Boverhof,

Redekop,

Bos

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

Objective To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. Methods This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury’s imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. Results RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI’s lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. Conclusion The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. Critical relevance statement The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. Keypoints • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.

show abstract

“…This can be done by datamining the EHR but also by processing real‐time data from participants that are collected by trackers, such as smartwatches and other wearables. Finally, AI approaches can help to collect real‐world evidence by mining EHRs for relevant data (as described below), as well as by processing these data into clinically meaningful outcomes 16,17 . An example are the abundance of AI‐based approaches available for automated processing of imaging data which are shown to have similar accuracy rates as a radiologist.…”

Section: Clinical Trials and Real‐world Evidencementioning

confidence: 99%

“…Finally, AI approaches can help to collect real-world evidence by mining EHRs for relevant data (as described below), as well as by processing these data into clinically meaningful outcomes. 16,17 An example are the abundance of AI-based approaches available for automated processing of imaging data which are shown to have similar accuracy rates as a radiologist.…”

Section: Clinical Trials and Real-world Evidencementioning

confidence: 99%

Artificial intelligence in pharmacology research and practice

Lee

Swen

2022

Clinical Translational Sci

View full text Add to dashboard Cite

In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real‐world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real‐world data. In this mini‐review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.

show abstract

The case for AI-driven cancer clinical trials – The efficacy arm in silico

Cited by 17 publications

References 32 publications

Digital twins for health: a scoping review

Digital twins for health: a scoping review

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice

Artificial intelligence in pharmacology research and practice

Contact Info

Product

Resources

About