Artificial Intelligence and Machine Learning Applied at the Point of Care

Angehrn, Zuzanna; Haldna, Liina; Zandvliet, Anthe S.; Berglund, Eva Gil; Zeeuw, Joost; Amzal, Billy; Cheung, S.Y. Amy; Polasek, Thomas M.; Pfister, Marc; Kerbusch, Thomas; Heckman, Niedre M.

doi:10.3389/fphar.2020.00759

Cited by 53 publications

(39 citation statements)

References 38 publications

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“…They could help in achieving precision dosing based on real-world evidence of clinical outcomes, which is superior to dosing determined from limited datasets in a conventional drug development paradigm. 33 With its capacity to leverage high-dimensional data and describe nonlinear relationships, ML could serve as a powerful tool for pharmacometric analysis. 14 Another area in which AI can boost therapeutic opportunities for rare diseases is "drug repositioning" also known as "drug repurposing."…”

Section: Opportunities For Clinical Pharmacology To Boost Orphan Drug...mentioning

confidence: 99%

“…AI/ML developments are likely to impact clinical pharmacologists in the next decade. They could help in achieving precision dosing based on real‐world evidence of clinical outcomes, which is superior to dosing determined from limited datasets in a conventional drug development paradigm 33 . With its capacity to leverage high‐dimensional data and describe nonlinear relationships, ML could serve as a powerful tool for pharmacometric analysis 14 …”

Section: Opportunities For Clinical Pharmacology To Boost Orphan Drug...mentioning

confidence: 99%

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Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Abuasal

Ahmed

Patel

et al. 2021

Clin Pharma and Therapeutics

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Several challenges are associated with rare disease drug development in neurology. In this article, we summarize the US Food and Drug Administration’s experience with clinical drug development for rare neurological diseases and discuss clinical pharmacology’s critical contributions to drug development for rare diseases. We used publicly available information to identify and screen drug products approved for rare neurological indications between 1983 and 2019. We highlighted cases in which clinical pharmacology contributed to the evidence of drug efficacy, dose selection for pivotal clinical trials, dose optimization based on intrinsic and extrinsic factors, pharmacokinetic bridging for formulations, and efficacy bridging across different racial groups. Fifty‐one approved drug products were identified since the introduction of the Orphan Drug Act in 1983. Interestingly, the number of approvals in the last few years increased significantly, probably due to advances in genomic research and targeted drug modalities. Evaluation of dose selection in patient populations showed that in 52% of cases, the sponsors did not evaluate efficacy for more than one or two dose levels throughout the development program. Clinical pharmacology studies to evaluate the effect of intrinsic or extrinsic factors were adequately characterized in most of the applications. With the expansion of model informed drug development applications, (e.g., quantitative systems pharmacology and deep learning neural network models), the role and impact of clinical pharmacology is expected to grow exponentially in the next decade and enhance the development of novel treatment modalities for neurological rare diseases.

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Section: Opportunities For Clinical Pharmacology To Boost Orphan Drug...mentioning

confidence: 99%

Section: Opportunities For Clinical Pharmacology To Boost Orphan Drug...mentioning

confidence: 99%

Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Abuasal

Ahmed

Patel

et al. 2021

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

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“…From the beginning, this work emphasized real-world conditions [36][37][38]40,41,[43][44][45][46] by (1) Utilization of large datasets representing multiple geographically dispersed sites for model development;…”

Section: Uniqueness Of Llied Use-case and Developed Ai Modelmentioning

confidence: 99%

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

et al. 2022

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Purpose: Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "predeployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions.Approach: A two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model. With the unexpected early appearance of LLIED types during simulated real-world trialing, retraining of a newer 12-class version preceded retrialing. A zero footprint (ZF) graphical user interface (GUI)/viewer with DICOM-based output was developed for inference-result display and adjudication, supporting end-user engagement and model continuous learning and/or modernization.Results: During model testing or trialing using both the nine-class and 12-class models, robust detection/localization was consistently 100%, with mAP 0.99 from fivefold cross-validation. Safety-level categorization was high during both testing (AUC ≥ 0.98 and ≥0.99, respectively) and trialing (accuracy 98% and 97%, respectively). LLIED-type identifications by the two models during testing (1) were 98.9% and 99.5% overall correct and (2) consistently showed AUC ≥ 0.92 (1.00 for 8/9 and 9/12 LLIED-types, respectively). Pre-deployment trialing of both models demonstrated overall type-identification accuracies of 94.5% and 95%, respectively. Of the small number of misidentifications, none involved MRI-stringently conditional or MRIunsafe types of LLIEDs. Optimized ZF GUI/viewer operations led to greater user-friendliness for radiologist engagement.Conclusions: Our LLIED-related AI methodology supports (1) 100% detection sensitivity, (2) high identification (including MRI-safety) accuracy, and (3) future model deployment with facilitated inference-result display and adjudication for ongoing model adaptation to future realworld experiences.

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“…For instance, in the intensive care unit (ICU), the application of AI wireless sensors can effectively collect patient information, reduce false alarms, and relieve challenges in the ICU [105] . With the gradual diversification of AI technology, there have been many new tools (monitoring and remote management) in the field of nursing [106] . The AI-based medical devices can be helpful during patient recovery, meeting the requirements of rehabilitation and expediting the proceedings [107] .…”

Section: Rehabilitation Assistancementioning

confidence: 99%

Application of Artificial Intelligence in Medicine: An Overview

et al. 2021

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Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

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Artificial Intelligence and Machine Learning Applied at the Point of Care

Abstract: Conclusions: The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

Cited by 53 publications

References 38 publications

Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

Application of Artificial Intelligence in Medicine: An Overview

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