Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

Rho, Mi Jung; Park, Jihwan; Moon, Hyong Woo; Lee, Chanjung; Nam, Sejin; Kim, Dongbum; Kim, Choung‐Soo; Jeon, Seong Soo; Kang, Minyong; Lee, Ji Youl

doi:10.1371/journal.pone.0236553

Cited by 7 publications

(11 citation statements)

References 32 publications

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“…(1) ante-hoc methods: transparency interpretability with the aims of revealing the inner working mechanism or transparent structure of the entire model and (2) post hoc methods: interpretation for a specifc decision or outcome [57]. By Lipton's classifcation, the ante-hoc categorized various methods with respect to the type of interpretation: decision tree [22,30], decision rule [17,53,55], fuzzy inference [19,54], Bayesian models [2], and logistic regression [4,7,29]; post hoc methods were divided into feature importance [8,52], sensitivity analysis [3,8], visualization [20,50,51], and activation maximization [9]. In essence, knowledge-based AI models and white box models are referred to as ante-hoc methods.…”

Section: Interpretation Methods Of Cdss Lipton Classifed the Interpre...mentioning

confidence: 99%

“…Black box models are often referring to data-driven AI, such as support vector machine [3,20,52,56], random forest [7,8,50], and deep learning [8,9,15]. Although the internal working mechanism of these models is difcult to understand, black box models can handle a huge scale of complex and interrelated data with higher performance than that of the white box model and knowledge-based AI models [3,11].…”

Section: Model Of Cdss Based On Aimentioning

confidence: 99%

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Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

Xie

Liao

et al. 2023

Journal of Healthcare Engineering

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Background. Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective. This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods. A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results. Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions. The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.

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Section: Interpretation Methods Of Cdss Lipton Classifed the Interpre...mentioning

confidence: 99%

Section: Model Of Cdss Based On Aimentioning

confidence: 99%

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

Xie

Liao

et al. 2023

Journal of Healthcare Engineering

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“…Among three SWs, we focused on clinical SW. LifeSemantics Corp. developed the clinical SW for PCa ( Fig. 1 ), 7 which is a company in the digital health platform business using AI. 11 , 12 The clinical SW screen is largely divided into a patient information section and a clinical outcome prediction section.…”

Section: Methodsmentioning

confidence: 99%

“…5 , 6 The Dr. Answer AI project has 26 participating universities, associate general and general hospitals, and 22 companies focusing on eight diseases, including cardio-cerebral vascular disease, heart disease, breast cancer, colon cancer, prostate cancer (PCa), dementia, epilepsy, and incurable childhood genetic diseases. 7 The Dr. Answer project aims to secure AI learning medical data for AI SW development to improve the diagnosis and treatment of major diseases.…”

Section: Introductionmentioning

confidence: 99%

Dr. Answer AI for prostate cancer: Intention to use, expected effects, performance, and concerns of urologists

Rho

Park

Moon

et al. 2022

Prostate International

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“…A total of 27 medical institutions and 17 medical-related information technology companies are participating. PROMISE-P is a deep learning-based CAD software for assistance in prostate cancer diagnosis via a prostate needle biopsy [12,13]. PROMISE-P automatically analyzes the digital slide images of the prostate needle biopsy as follows: the software informs the user about the presence or absence of cancer and displays the high cancer risk area as a heatmap.…”

Section: Dr Answer™ Project and Promise-p Softwarementioning

confidence: 99%

Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy

et al. 2021

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Digital pathology systems (DPSs) have been globally implemented, and computer-assisted diagnosis (CAD) software has been actively developed in recent years. This study aimed to investigate perceptions of digital pathology and the demand for CAD. An online survey involving members of the Korean Society of Pathologists was conducted, and a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD. One hundred sixty-four Korean pathologists (13.6% of 1210 Korean pathologists) participated. The majority (77.4%) answered affirmatively regarding the necessity of implementing a DPS, and 26.8% had plans to implement or increase the use of DPSs in the following 2–3 years at their medical institutions. Pathologists felt that multidisciplinary care or conference accessibility (56.7%), remote consultation (49.4%), and big data building (32.9%) were useful parts of DPSs. Most pathologists (81.7%) responded that CAD software would assist with the diagnostic process. In a prostate needle biopsy, pathologists used the software to improve the measurement of tumor volume and/or length and core length but not to suggest a diagnostic name or Gleason grade. Korean pathologists who participated in the survey had highly positive perceptions of digital pathology and maintained a positive attitude toward the use of CAD software.

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Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

Cited by 7 publications

References 32 publications

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

Dr. Answer AI for prostate cancer: Intention to use, expected effects, performance, and concerns of urologists

Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy

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