Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

By S. Park, Kyunghwa Han. Radiology, 2018

777 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/34e22341d14ae680aae1da06f9ce6bf937058097

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In Artificial Intelligence

In Health Informatics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2023, positive): “Initial efforts have focused on providing recommendations for the reporting of AI studies for different medical domains or clinical tasks (eg, TRIPOD+AI, 11 CLAIM, 12 CONSORT-AI, 13 DECIDE-AI, 14 PROBAST-AI, 15 CLEAR 16 ).”
Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers (2019, methodology): “Therefore, individuals developing such AI algorithms might rely on whatever data are available (methodologically referred to as convenience case-control data), although these may be prone to selection biases and artificial disease prevalence and likely not represent real-world clinical settings well”
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge (2019, background): “This helps to ameliorate some concern over the external validity of a single institution retrospective cohort like KiTS19, but in future work, a multi-institution cohort with a prospectively collected test set would be preferable (Park and Han, 2018).”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, methodology): “…of the AIPM, external AIPM model validation can be based on discrimination (area under the receiver operating characteristic curve, sensitivity, speci ﬁ city, positive and negative predictive values), calibration (calibration plot) 5,6,13,17,49,56,78 , and Decision Curve Analysis 5,6,17,49,68,72 .”
Applications of machine learning and deep learning to thyroid imaging: where do we stand? (2020, background): “However, at this point, the use of AI tools in clinical practice is of great concern since most studies were designed as proof-ofconcept or technical feasibility research without a thorough external validation of real-world clinical performance [14-16].”
The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. (2019, neutral): “82 86 The diagnostic performance of the models in these pre-clinical studies exhibited satisfactory results, but these results might be overly optimistic due to the training-testing images presenting obvious lesions from extracted teeth and lacking the image of other oral tissues.”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “Temporal validation (newly recruited participants) and geographical validation (data from a different site) are more rigorous strategies to ensure clinical utility (see (87,88)).”
Preparing Medical Imaging Data for Machine Learning (2020, background): “This is especially true in the clinical setting and is well outlined by Park and Han (22).”
Predicting cancer outcomes with radiomics and artificial intelligence in radiology (2021, background): “Data from retrospectively acquired data sets are often most convenient to aggregate, but raise challenges related to data purity for both model training and validation because predefined inclusion and exclusion criteria might result in unconscious biases in AI algorithms 144 .”
Convolutional neural networks: an overview and application in radiology (2018, background): “It is worthy of mention that the term Bvalidation^ is used differently in the medical field and the machine learning field [26].”