Rise of the Machines: Artificial Intelligence and the Clinical Laboratory

Haymond, Shannon; McCudden, Christopher R.

doi:10.1093/jalm/jfab075

Cited by 25 publications

(10 citation statements)

References 34 publications

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“… 4 Key examples of AI application in anatomic pathology (AP) include automated assessment of prognostic biomarkers such as Ki-67 in breast cancer, 9 tumour grading in prostate cancer, 10 , 11 diagnosis of metastatic breast cancer in lymph nodes, 12 and optimization of clinical laboratory workflows, such as automated quality control (QC). 13 , 14 …”

Section: Introductionmentioning

confidence: 99%

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

Berbís¹,

McClintock²,

Bychkov³

et al. 2023

eBioMedicine

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Section: Introductionmentioning

confidence: 99%

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

Berbís¹,

McClintock²,

Bychkov³

et al. 2023

eBioMedicine

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“…In healthcare, machine-learning (ML) models are used for various tasks, such as image and signal analysis, disease diagnosis, treatment planning, and drug discovery (1). The use of ML models to improve patient care is a novel approach, but its implementation in clinical practice is still limited (2).…”

Section: Introductionmentioning

confidence: 99%

How to explain a machine learning model: HbA1c classification example

Topçu

2023

Journal of Medicine and Palliative Care

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Aim: Machine learning tools have various applications in healthcare. However, the implementation of developed models is still limited because of various challenges. One of the most important problems is the lack of explainability of machine learning models. Explainability refers to the capacity to reveal the reasoning and logic behind the decisions made by AI systems, making it straightforward for human users to understand the process and how the system arrived at a specific outcome. The study aimed to compare the performance of different model-agnostic explanation methods using two different ML models created for HbA1c classification. Material and Method: The H2O AutoML engine was used for the development of two ML models (Gradient boosting machine (GBM) and default random forests (DRF)) using 3,036 records from NHANES open data set. Both global and local model-agnostic explanation methods, including performance metrics, feature important analysis and Partial dependence, Breakdown and Shapley additive explanation plots were utilized for the developed models. Results: While both GBM and DRF models have similar performance metrics, such as mean per class error and area under the receiver operating characteristic curve, they had slightly different variable importance. Local explainability methods also showed different contributions to the features. Conclusion: This study evaluated the significance of explainable machine learning techniques for comprehending complicated models and their role in incorporating AI in healthcare. The results indicate that although there are limitations to current explainability methods, particularly for clinical use, both global and local explanation models offer a glimpse into evaluating the model and can be used to enhance or compare models.

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“…However, interpreting clinical tests individually may cause a misleading diagnosis [ 31 ]. The laboratory test results can be interpreted with experienced clinicians, but it can also be integrated and interpreted with artificial intelligence (AI), such as machine learning algorithms [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

et al. 2022

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Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

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Rise of the Machines: Artificial Intelligence and the Clinical Laboratory

Cited by 25 publications

References 34 publications

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

How to explain a machine learning model: HbA1c classification example

Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

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