Artificial intelligence in managing clinical trial design and conduct

Bhatt, Arun

doi:10.4103/picr.picr_312_20

Cited by 21 publications

(13 citation statements)

References 12 publications

(10 reference statements)

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“…10,24 As such, there are some examples of how patient enrichment methodologies are being explored, particularly in oncology. 11,[25][26][27] This study demonstrates how AI could identify optimal candidates based on their predicted response to the treatment available on the market. It may thus contribute to the success of future clinical trials for novel drugs and is likely to save time when treating patients.…”

Section: Discussionmentioning

confidence: 99%

“…Arti cial intelligence (AI) systems can be used to aid patient selection for a clinical trial by using the patient's predicted response to the trialled drug (predictive enrichment) and/or predicted clinical outcome (prognostic enrichment). [10][11][12] In this rst of a series of PRECISE study reports, we present and evaluate the e cacy of an AI-based cohort selection tool for the identi cation of patients for an enriched trial cohort based on their predicted suboptimal response to loading phase a ibercept, de ned as persistent macular uid post loading-phase.…”

Section: Introductionmentioning

confidence: 99%

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A multi-modal AI-driven cohort selection tool based on response to loading-phase aflibercept for neovascular age-related macular degeneration: PRECISE study

Chorev

Haderlein

Chandra

et al. 2022

Preprint

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Patients diagnosed with neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent, aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-modal AI-driven cohort selection tool based on response to loading-phase aflibercept for neovascular age-related macular degeneration: PRECISE study

Chorev

Haderlein

Chandra

et al. 2022

Preprint

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“…Deep learning algorithms excel in tasks that require discerning non-linear dependencies, provided a sufficiently large training set is provided and the task is well formalized. In application to life sciences, deep learning has been demonstrated to successfully solve the tasks of pharmaceutical lead selection and drug design, clinical trial design, as well as diagnostic imaging and electrodiagnosis [28][29][30][31][32]. AI systems have a virtually unlimited bandwidth, compared to human professionals, and can deliver their reports in a matter of seconds, thus enabling more affordable and scalable healthcare [33].…”

Section: The Ai Approach and Aging Clocksmentioning

confidence: 99%

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

Galkin¹,

Kochetov²,

Keller³

et al. 2022

Aging

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In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being.

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“…In addition deploying AI in research could result in the discovery of novel treatments and also aid in mapping the underlying mechanisms, markers, and progression of diseases (44). AI can also help in improving the design and conduct of clinical trials by helping in patient selection and recruitment (45).…”

Section: Emerging Opportunitiesmentioning

confidence: 99%

Scaling up artificial intelligence to curb infectious diseases in Africa

Otaigbe

2022

Front. Digit. Health

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Artificial intelligence in managing clinical trial design and conduct

Cited by 21 publications

References 12 publications

A multi-modal AI-driven cohort selection tool based on response to loading-phase aflibercept for neovascular age-related macular degeneration: PRECISE study

A multi-modal AI-driven cohort selection tool based on response to loading-phase aflibercept for neovascular age-related macular degeneration: PRECISE study

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

Scaling up artificial intelligence to curb infectious diseases in Africa

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