Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

Bizzo, Bernardo C.; Dasegowda, Giridhar; Bridge, Christopher P.; Miller, Benjamin F.; Hillis, James; Kalra, Mannudeep K.; Durniak, Kimberly; Stout, Markus; Schultz, Thomas; Alkasab, Tarik K.; Dreyer, Keith J.

doi:10.1016/j.jacr.2023.01.002

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…Unlike ML, human clinical decision making is tied to context [36,45]. The black-box nature of ML can make it difficult to detect biases, understand, or trust the results [43,46]. Although there are ongoing efforts to provide explainability to ML models, there are limitations and drawbacks to explainability techniques [47,48].…”

Section: Central Importance Of Humansmentioning

confidence: 99%

“…Physicians must understand what is expected of them in relation to error reporting and tracking. A framework for continuous tracking and reporting of errors in clinical AI prediction tools is especially important with ML, due to lack of model transparency, including for some Food and Drug Administration-approved ML tools [43].…”

Section: Expect Artificial Intelligence Errorsmentioning

confidence: 99%

See 1 more Smart Citation

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Monteith,

Glenn,

Geddes

et al. 2023

Pharmacopsychiatry

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This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.

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Section: Central Importance Of Humansmentioning

confidence: 99%

Section: Expect Artificial Intelligence Errorsmentioning

confidence: 99%

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Monteith,

Glenn,

Geddes

et al. 2023

Pharmacopsychiatry

View full text Add to dashboard Cite

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“…56 Algorithms require extensive multi-institutional testing and validation to ensure generalizability over heterogeneous imaging data and diverse patient populations Moreover, the seamless integration of these technologies into clinical workflows, coupled with ongoing deliberations surrounding their legal and ethical implications, represents additional hurdles for the responsible deployment of AI technologies in medical practice. 57,58 In conclusion, we are witnessing remarkable advances in glial tumor diagnosis, significantly improving our ability to classify these tumors more accurately. Our diagnostic and prognostic capabilities have grown substantially due to advances in tumor imaging and clinical pathology/genetics.…”

Section: Artificial Intelligence Machine Learning Radiomics and Radio...mentioning

confidence: 99%

Section: Artificial Intelligence Machine Learning Radiomics and Radio...mentioning

confidence: 99%

Advances in diffuse glial tumors diagnosis

Godoy,

Paes,

Ayres

et al. 2023

Arq Neuropsiquiatr

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In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

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“…[2] One cause of this is that hospitals are cost strapped and still reeling from pandemic, and a tiny fraction of FDA-approved AI devices are covered by insurance. [3] A perhaps even bigger and more serious issue is that external validations of AI algorithms often show substantial drops in performance compared to what was originally reported in the FDA submission. [4, 5, 6] This degradation is understood to be caused by “distribution shift”.…”

Section: Introductionmentioning

confidence: 99%

No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

Elton,

Dasegowda,

Sato

et al. 2024

Preprint

Self Cite

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Machine learning models can assist clinicians and researchers in many tasks within radiology such as diagnosis, triage, segmentation/measurement, and quality assurance. To better leverage machine learning we have developed a platform that allows users to label data and train models without requiring any programming knowledge. The technology stack consists of a TypeScript web application running on .NET for user interaction, Python, PyTorch, and MONAI for machine learning, DICOM WADO-RS to retrieve data from clinical systems, and Docker for model management. As a first trial of the system, researchers used it to train a model for clavicle fracture detection as part of an IRB-approved retrospective study. The researchers labeled 4,135 clavicle radiographs from 2,039 patients across 13 sites. The platform automatically split the data into training, validation, and test sets and trained a model until the validation loss plateaued. The system then returned a receiver operating characteristic curve, AUC, F1, and other metrics. The resulting model identifies clavicle fractures with 90% sensitivity, 87% specificity, and 88% accuracy with an AUC of 0.95. This model performance is equivalent to or better than similar models reported in the literature. More recently, our system was used to train a model to identify if ultrasound frames that contain personally identifiable information (PII). After validation, the model was used to help de-identify a large dataset that was to be used for research. This first-of-its-kind system streamlines model development and deployment and opens up an exciting new pathway for the use of AI within healthcare.

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Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

Cited by 19 publications

References 13 publications

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Advances in diffuse glial tumors diagnosis

No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

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