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Article ; Online: Artificial Intelligence in Medicine

Seong Ho Park

대한영상의학회지, Vol 78, Iss 5, Pp 301-

Beginner’s Guide

2018 Volume 308

Abstract: Artificial intelligence is expected to influence clinical practice substantially in the foreseeable future. Despite all the excitement around the technology, it cannot be denied that the application of artificial intelligence in medicine is overhyped. In ...

Abstract	Artificial intelligence is expected to influence clinical practice substantially in the foreseeable future. Despite all the excitement around the technology, it cannot be denied that the application of artificial intelligence in medicine is overhyped. In fact, artificial intelligence for medicine is presently in its infancy, and very few are currently in clinical use. To best leverage the potential of this technology to improve patient care, clinicians need to see beyond the hype, as the guidance and leadership of medical professionals are critical in this matter. To this end, medical professionals must understand the underlying technological basics of artificial intelligence, as well as the methodologies of its proper clinical validation. They should also have an impartial, complete view of the capabilities, pitfalls, and limitations of the technology and its use in healthcare. The present article provides succinct explanations of these matters and suggests further reading materials (peer-reviewed articles and web pages) for medical professionals who are unfamiliar with artificial intelligence.
Keywords	artificial intelligence ; machine learning ; medicine ; medical imaging ; diagnoses ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Subject code	401
Language	English
Publishing date	2018-05-01T00:00:00Z
Publisher	The Korean Society of Radiology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Survey of the Knowledge of Korean Radiology Residents on Medical Artificial Intelligence

Hyeonbin Lee / Seong Ho Park / Cherry Kim / Seungkwan Kim / Jaehyung Cha

대한영상의학회지, Vol 81, Iss 6, Pp 1397-

2020 Volume 1411

Abstract: Purpose To survey the perception, knowledge, wishes, and expectations of Korean radiology residents regarding artificial intelligence (AI) in radiology. Materials and Methods From June 4th to 7th, 2019, questionnaires comprising 19 questions related to ... ...

Abstract	Purpose To survey the perception, knowledge, wishes, and expectations of Korean radiology residents regarding artificial intelligence (AI) in radiology. Materials and Methods From June 4th to 7th, 2019, questionnaires comprising 19 questions related to AI were distributed to 113 radiology residents. Results were analyzed based on factors such as the year of residency and location and number of beds of the hospital. Results A total of 101 (89.4%) residents filled out the questionnaire. Fifty (49.5%) respondents had studied AI harder than the average while 68 (67.3%) had a similar or higher understanding of AI than the average. In addition, the self-evaluation and knowledge level of AI were significantly higher for radiology residents at hospitals located in Seoul and Gyeonggi-do compared to radiology residents at hospitals located in other regions. Furthermore, the self-evaluation and knowledge level of AI were significantly lower in junior residents than in residents in the 4th year of training. Of the 101 respondents, only 16 (15.8%) had experiences in AI-related study while 91 (90%) were willing to participate in AI-related study in the future. Conclusion Organizational efforts through a radiology society would be needed to meet the need of radiology trainees for AI education and to promote the role of radiologists more adequately in the era of medical AI.
Keywords	surveys and questionnaires ; artificial intelligence ; machine learning ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Subject code	710
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	The Korean Society of Radiology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CRISPR-clear imaging of melanin-rich B16-derived solid tumors

Rajib Schubert / Taegeun Bae / Branko Simic / Sheena N. Smith / Seong-Ho Park / Gabriela Nagy-Davidescu / Viviana Gradinaru / Andreas Plückthun / Junho K. Hur

Communications Biology, Vol 6, Iss 1, Pp 1-

2023 Volume 8

Abstract: Tyrosinase CRISPR knock out in the B16 melanoma cell line eliminates melanin production, with the lack of pigment not affecting tumour engraftment or growth and allowing for 3D imaging after clearing by PACT. ...

Abstract	Tyrosinase CRISPR knock out in the B16 melanoma cell line eliminates melanin production, with the lack of pigment not affecting tumour engraftment or growth and allowing for 3D imaging after clearing by PACT.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: What should medical students know about artificial intelligence in medicine?

Seong Ho Park / Kyung-Hyun Do / Sungwon Kim / Joo Hyun Park / Young-Suk Lim

Journal of Educational Evaluation for Health Professions, Vol

2019 Volume 16

Abstract: Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical ... ...

Abstract	Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.
Keywords	artificial intelligence ; machine learning ; deep learning ; medical students ; Special aspects of education ; LC8-6691 ; Medicine ; R
Subject code	020
Language	English
Publishing date	2019-07-01T00:00:00Z
Publisher	Korea Health Insurance Licensing Examination Institute
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review

Seong Ho Park / Young-Hak Kim / Jun Young Lee / Soyoung Yoo / Chong Jai Kim

Science Editing, Vol 6, Iss 2, Pp 91-

2019 Volume 98

Abstract: This review article aims to highlight several areas in research studies on artificial intelligence (AI) in medicine that currently require additional transparency and explain why additional transparency is needed. Transparency regarding training data, ... ...

Abstract	This review article aims to highlight several areas in research studies on artificial intelligence (AI) in medicine that currently require additional transparency and explain why additional transparency is needed. Transparency regarding training data, test data and results, interpretation of study results, and the sharing of algorithms and data are major areas for guaranteeing ethical standards in AI research. For transparency in training data, clarifying the biases and errors in training data and the AI algorithms based on these training data prior to their implementation is critical. Furthermore, biases about institutions and socioeconomic groups should be considered. For transparency in test data and test results, authors should state if the test data were collected externally or internally and prospectively or retrospectively at first. It is necessary to distinguish whether datasets were convenience samples consisting of some positive and some negative cases or clinical cohorts. When datasets from multiple institutions were used, authors should report results from each individual institution. Full publication of the results of AI research is also important. For transparency in interpreting study results, authors should interpret the results explicitly and avoid over-interpretation. For transparency by sharing algorithms and data, sharing is required for replication and reproducibility of the research by other researchers. All of the above mentioned high standards regarding transparency of AI research in healthcare should be considered to facilitate the ethical conduct of AI research.
Keywords	Artificial intelligence ; Ethics ; Research ; Publishing ; Bias ; Science (General) ; Q1-390
Subject code	170
Language	English
Publishing date	2019-08-01T00:00:00Z
Publisher	Korean Council of Science Editors
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Low-dose abdominopelvic computed tomography in patients with lymphoma

Sungjin Yoon / Kwai Han Yoo / So Hyun Park / Hawk Kim / Jae Hoon Lee / Jinny Park / Seong Ho Park / Hwa Jung Kim

PLoS ONE, Vol 17, Iss 8, p e

An image quality and radiation dose reduction study.

2022 Volume 0272356

Abstract: This study aimed to evaluate image quality, the detection rate of enlarged lymph nodes, and radiation dose exposure of ultralow-dose and low-dose abdominopelvic computed tomography (CT) in patients with lymphoma. Patients with lymphoma who underwent ... ...

Abstract	This study aimed to evaluate image quality, the detection rate of enlarged lymph nodes, and radiation dose exposure of ultralow-dose and low-dose abdominopelvic computed tomography (CT) in patients with lymphoma. Patients with lymphoma who underwent abdominopelvic CT using dual-source scanner were retrospectively recruited from a single center. CT images were obtained at 90 kVp dual-source mode reformatted in three data sets using the advanced modelled iterative reconstruction algorithm: 100% (standard-dose CT), 66.7% (low-dose CT), and 33.3% (ultralow-dose CT). Two radiologists analyzed subjective image quality and detection of abdominal enlarged lymph nodes on ultralow-dose, low-dose, and standard-dose CT blindly and independently. The results were compared with reference standards. Three readers (two radiologists and one hematologist) reviewed overall image quality and spleen size. In total, 128 consecutive CT scans (77 complete response, 44 partial response, 6 progressive disease, and 1 initial evaluation) from 86 patients (64 B-cell lymphoma, 14 T/NK-cell lymphoma, and 8 Hodgkin's lymphoma cases) were assessed. The enlarged lymph node-based detection rates for two readers were 97.0% (96/99) and 94.0% (93/99) on standard-dose CT, 97.0% (96/99) and 94.0% (93/99) on low-dose CT, and 94.0% (93/99) and 89.9% (89/99) on ultralow-dose CT. Overall image quality was 3.8 ± 0.5, 3.9 ± 0.5, and 4.1 ± 0.5 on ultralow-dose CT; 4.7 ± 0.4, 4.6 ± 0.5, and 4.8 ± 0.3 on low-dose CT; and 4.8 ± 0.4, 4.7 ± 0.4, and 4.9 ± 0.2 on standard-dose CT, according to two radiologists and one hematologist, respectively. Intraclass correlation coefficients of spleen size were 0.90 (95% confidence interval [CI], 0.87-0.93), 0.91 (95% CI, 0.88-0.93), and 0.91 (95% CI, 0.88-0.93) on ultralow-dose, low-dose, and standard-dose CT, respectively. Mean effective radiation doses of standard-dose, low-dose, and ultralow-dose CT were 5.7 ±1.8 mSv, 3.8 ± 1.2 mSv, and 1.9 ± 0.6 mSv, respectively. Our findings suggest that ultralow-dose and low-dose CT, ...
Keywords	Medicine ; R ; Science ; Q
Subject code	610 ; 616
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dual-source abdominopelvic computed tomography

Seung Joon Choi / Su Joa Ahn / So Hyun Park / Seong Ho Park / Seong Yong Pak / Jae Won Choi / Young Sup Shim / Yu Mi Jeong / Bohyun Kim

PLoS ONE, Vol 15, Iss 9, p e

Comparison of image quality and radiation dose of 80 kVp and 80/150 kVp with tin filter.

2020 Volume 0231431

Abstract: Objective To compare the radiation dose and the objective and subjective image quality of 80 kVp and 80/150 kVp with tin filter (80/Sn150 kVp) computed tomography (CT) in oncology patients. Methods One-hundred-and-forty-five consecutive oncology patients ...

Abstract	Objective To compare the radiation dose and the objective and subjective image quality of 80 kVp and 80/150 kVp with tin filter (80/Sn150 kVp) computed tomography (CT) in oncology patients. Methods One-hundred-and-forty-five consecutive oncology patients who underwent third-generation dual-source dual-energy CT of the abdomen for evaluation of malignant visceral, peritoneal, extraperitoneal, and bone tumor were retrospectively recruited. Two radiologists independently reviewed each observation in 80 kVp CT and 80/Sn150 kVp CT. Modified line-density profile of the tumor and contrast-to-noise ratio (CNR) were measured. Diagnostic confidence, lesion conspicuity, and subjective image quality were calculated and compared between image sets. The effective dose and size-specific dose estimate (SSDE) were calculated in the image sets. Results Modified line-density profile analysis revealed higher attenuation differences between the tumor and normal tissue in 80 kVp CT than in 80/Sn150 kVp CT (127 vs. 107, P = 0.05). The 80 kVp CT showed increased CNR in the liver (8.0 vs. 7.6) and the aorta (18.9 vs. 16.3) than the 80/Sn150 kVp CT. The 80 kVp CT yielded higher enhancement of organs (4.9 ± 0.2 vs. 4.7 ± 0.4, P<0.001) and lesion conspicuity (4.9 ± 0.3 vs. 4.8 ± 0.5, P = 0.035) than the 80/Sn150 kVp CT; overall image quality and confidence index were comparable. The effective dose was reduced by 45.2% with 80 kVp CT (2.3 mSv ± 0.9) compared to 80/Sn150 kVp CT (4.1 mSv ± 1.5). The SSDE was 7.4 ± 3.8 mGy on 80/Sn150 kVp CT and 4.1 ± 2.2 mGy on 80 kVp CT. Conclusions The 80 kVp CT reduced the radiation dose by 45.2% in oncology patients while showing comparable or superior image quality to that of 80/Sn150 kVp CT for abdominal tumor evaluation.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inconsistency in the use of the term “validation” in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging

Dong Wook Kim / Hye Young Jang / Yousun Ko / Jung Hee Son / Pyeong Hwa Kim / Seon-Ok Kim / Joon Seo Lim / Seong Ho Park / Julian C. Hong

PLoS ONE, Vol 15, Iss

2020 Volume 9

Abstract: Background The development of deep learning (DL) algorithms is a three-step process—training, tuning, and testing. Studies are inconsistent in the use of the term “validation”, with some using it to refer to tuning and others testing, which hinders ... ...

Abstract	Background The development of deep learning (DL) algorithms is a three-step process—training, tuning, and testing. Studies are inconsistent in the use of the term “validation”, with some using it to refer to tuning and others testing, which hinders accurate delivery of information and may inadvertently exaggerate the performance of DL algorithms. We investigated the extent of inconsistency in usage of the term “validation” in studies on the accuracy of DL algorithms in providing diagnosis from medical imaging. Methods and findings We analyzed the full texts of research papers cited in two recent systematic reviews. The papers were categorized according to whether the term “validation” was used to refer to tuning alone, both tuning and testing, or testing alone. We analyzed whether paper characteristics (i.e., journal category, field of study, year of print publication, journal impact factor [JIF], and nature of test data) were associated with the usage of the terminology using multivariable logistic regression analysis with generalized estimating equations. Of 201 papers published in 125 journals, 118 (58.7%), 9 (4.5%), and 74 (36.8%) used the term to refer to tuning alone, both tuning and testing, and testing alone, respectively. A weak association was noted between higher JIF and using the term to refer to testing (i.e., testing alone or both tuning and testing) instead of tuning alone (vs. JIF <5; JIF 5 to 10: adjusted odds ratio 2.11, P = 0.042; JIF >10: adjusted odds ratio 2.41, P = 0.089). Journal category, field of study, year of print publication, and nature of test data were not significantly associated with the terminology usage. Conclusions Existing literature has a significant degree of inconsistency in using the term “validation” when referring to the steps in DL algorithm development. Efforts are needed to improve the accuracy and clarity in the terminology usage.
Keywords	Medicine ; R ; Science ; Q
Subject code	690
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inconsistency in the use of the term "validation" in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging.

Dong Wook Kim / Hye Young Jang / Yousun Ko / Jung Hee Son / Pyeong Hwa Kim / Seon-Ok Kim / Joon Seo Lim / Seong Ho Park

PLoS ONE, Vol 15, Iss 9, p e

2020 Volume 0238908

Abstract: Background The development of deep learning (DL) algorithms is a three-step process-training, tuning, and testing. Studies are inconsistent in the use of the term "validation", with some using it to refer to tuning and others testing, which hinders ... ...

Abstract	Background The development of deep learning (DL) algorithms is a three-step process-training, tuning, and testing. Studies are inconsistent in the use of the term "validation", with some using it to refer to tuning and others testing, which hinders accurate delivery of information and may inadvertently exaggerate the performance of DL algorithms. We investigated the extent of inconsistency in usage of the term "validation" in studies on the accuracy of DL algorithms in providing diagnosis from medical imaging. Methods and findings We analyzed the full texts of research papers cited in two recent systematic reviews. The papers were categorized according to whether the term "validation" was used to refer to tuning alone, both tuning and testing, or testing alone. We analyzed whether paper characteristics (i.e., journal category, field of study, year of print publication, journal impact factor [JIF], and nature of test data) were associated with the usage of the terminology using multivariable logistic regression analysis with generalized estimating equations. Of 201 papers published in 125 journals, 118 (58.7%), 9 (4.5%), and 74 (36.8%) used the term to refer to tuning alone, both tuning and testing, and testing alone, respectively. A weak association was noted between higher JIF and using the term to refer to testing (i.e., testing alone or both tuning and testing) instead of tuning alone (vs. JIF <5; JIF 5 to 10: adjusted odds ratio 2.11, P = 0.042; JIF >10: adjusted odds ratio 2.41, P = 0.089). Journal category, field of study, year of print publication, and nature of test data were not significantly associated with the terminology usage. Conclusions Existing literature has a significant degree of inconsistency in using the term "validation" when referring to the steps in DL algorithm development. Efforts are needed to improve the accuracy and clarity in the terminology usage.
Keywords	Medicine ; R ; Science ; Q
Subject code	690
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Feasibility, safety, and adequacy of research biopsies for cancer clinical trials at an academic medical center.

Kyoungmin Lee / So Jung Lee / Shinkyo Yoon / Baek-Yeol Ryoo / Sang-We Kim / Sang Hyun Choi / Sang Min Lee / Eun Jin Chae / Yangsoon Park / Se-Jin Jang / Soo-Yeon Park / Young-Kwang Yoon / Seong Ho Park / Tae Won Kim

PLoS ONE, Vol 14, Iss 8, p e

2019 Volume 0221065

Abstract: Objective Research biopsies are an essential component of cancer clinical trials for studying drug efficacy and identifying biomarkers. Site-level clinical investigators, however, do not have access to results on the adequacy of research biopsies for ... ...

Abstract	Objective Research biopsies are an essential component of cancer clinical trials for studying drug efficacy and identifying biomarkers. Site-level clinical investigators, however, do not have access to results on the adequacy of research biopsies for histological or molecular assays, because samples are sent to central labs and the test results are seldom reported back to site-level investigators unless requested. We evaluated the feasibility, safety, and adequacy of research biopsies performed at an academic medical center. Materials and methods We retrospectively reviewed the data on 122 research biopsy sessions conducted in 99 patients via percutaneous core needle biopsy for 39 clinical trials from January 2017 to February 2018 at a single institute. We asked the sponsors of each clinical trial for the adequacy of the biopsy samples for histological or molecular assays. Results The biopsy success rate was 93.4% (113/122), with nine samples categorized as inadequate for obtaining pathologic diagnosis. Post-biopsy complications occurred in 9.8% (12/122) of biopsies, all of which were mild and completely recovered by the day after the biopsy. The sponsors of clinical trials provided feedbacks on the adequacy of 76 biopsy samples, and noted that a total of 8 biopsy samples from 7 patients were inadequate for analysis, resulting in an adequacy rate of 89.5% (68/76): the reasons for inadequacy were insufficient tumor content for immunohistochemistry (n = 3) and low RNA yield for sequencing (n = 5). Conclusion Research biopsies performed at an experienced, multidisciplinary center had acceptable safety for patients as well as practicality in terms of obtaining adequate tissue samples for molecular studies.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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