1
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Eltorai AEM, McKinney SE, Rockenbach MABC, Karuppiah S, Bizzo BC, Andriole KP. Primary care provider perspectives on the value of opportunistic CT screening. Clin Imaging 2024; 112:110210. [PMID: 38850710 DOI: 10.1016/j.clinimag.2024.110210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/10/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND Clinical adoption of AI applications requires stakeholders see value in their use. AI-enabled opportunistic-CT-screening (OS) capitalizes on incidentally-detected findings within CTs for potential health benefit. This study evaluates primary care providers' (PCP) perspectives on OS. METHODS A survey was distributed to US Internal and Family Medicine residencies. Assessed were familiarity with AI and OS, perspectives on potential value/costs, communication of results, and technology implementation. RESULTS 62 % of respondents (n = 71) were in Family Medicine, 64.8 % practiced in community hospitals. Although 74.6 % of respondents had heard of AI/machine learning, 95.8 % had little-to-no familiarity with OS. The majority reported little-to-no trust in AI. Reported concerns included AI accuracy (74.6 %) and unknown liability (73.2 %). 78.9 % of respondents reported that OS applications would require radiologist oversight. 53.5 % preferred OS results be included in a separate "screening" section within the Radiology report, accompanied by condition risks and management recommendations. The majority of respondents reported results would likely affect clinical management for all queried applications, and that atherosclerotic cardiovascular disease risk, abdominal aortic aneurysm, and liver fibrosis should be included within every CT report regardless of reason for examination. 70.5 % felt that PCP practices are unlikely to pay for OS. Added costs to the patient (91.5 %), the healthcare provider (77.5 %), and unknown liability (74.6 %) were the most frequently reported concerns. CONCLUSION PCP preferences and concerns around AI-enabled OS offer insights into clinical value and costs. As AI applications grow, feedback from end-users should be considered in the development of such technology to optimize implementation and adoption. Increasing stakeholder familiarity with AI may be a critical prerequisite first step before stakeholders consider implementation.
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Affiliation(s)
- Adam E M Eltorai
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Suzannah E McKinney
- Data Science Office, Mass General Brigham, Boston, MA, United States of America
| | | | - Saby Karuppiah
- Department of Family Medicine, HCA Healthcare, Kansas City, MO, United States of America
| | - Bernardo C Bizzo
- Data Science Office, Mass General Brigham, Boston, MA, United States of America
| | - Katherine P Andriole
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America; Data Science Office, Mass General Brigham, Boston, MA, United States of America.
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2
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Eibschutz L, Lu MY, Abbassi MT, Gholamrezanezhad A. Artificial intelligence in the detection of non-biological materials. Emerg Radiol 2024; 31:391-403. [PMID: 38530436 PMCID: PMC11130001 DOI: 10.1007/s10140-024-02222-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024]
Abstract
Artificial Intelligence (AI) has emerged as a transformative force within medical imaging, making significant strides within emergency radiology. Presently, there is a strong reliance on radiologists to accurately diagnose and characterize foreign bodies in a timely fashion, a task that can be readily augmented with AI tools. This article will first explore the most common clinical scenarios involving foreign bodies, such as retained surgical instruments, open and penetrating injuries, catheter and tube malposition, and foreign body ingestion and aspiration. By initially exploring the existing imaging techniques employed for diagnosing these conditions, the potential role of AI in detecting non-biological materials can be better elucidated. Yet, the heterogeneous nature of foreign bodies and limited data availability complicates the development of computer-aided detection models. Despite these challenges, integrating AI can potentially decrease radiologist workload, enhance diagnostic accuracy, and improve patient outcomes.
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Affiliation(s)
- Liesl Eibschutz
- Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA
| | - Max Yang Lu
- Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA
| | - Mashya T Abbassi
- Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA
| | - Ali Gholamrezanezhad
- Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA.
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3
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Hamilton A. Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus 2024; 16:e59747. [PMID: 38840993 PMCID: PMC11152357 DOI: 10.7759/cureus.59747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2024] [Indexed: 06/07/2024] Open
Abstract
The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.
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Affiliation(s)
- Allan Hamilton
- Artificial Intelligence Division, Arizona Simulation Technology and Education Center (ASTEC) University of Arizona, Tucson, USA
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4
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Lastrucci A, Wandael Y, Ricci R, Maccioni G, Giansanti D. The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review. Diagnostics (Basel) 2024; 14:939. [PMID: 38732351 PMCID: PMC11083654 DOI: 10.3390/diagnostics14090939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This study investigates, through a narrative review, the transformative impact of deep learning (DL) in the field of radiotherapy, particularly in light of the accelerated developments prompted by the COVID-19 pandemic. The proposed approach was based on an umbrella review following a standard narrative checklist and a qualification process. The selection process identified 19 systematic review studies. Through an analysis of current research, the study highlights the revolutionary potential of DL algorithms in optimizing treatment planning, image analysis, and patient outcome prediction in radiotherapy. It underscores the necessity of further exploration into specific research areas to unlock the full capabilities of DL technology. Moreover, the study emphasizes the intricate interplay between digital radiology and radiotherapy, revealing how advancements in one field can significantly influence the other. This interdependence is crucial for addressing complex challenges and advancing the integration of cutting-edge technologies into clinical practice. Collaborative efforts among researchers, clinicians, and regulatory bodies are deemed essential to effectively navigate the evolving landscape of DL in radiotherapy. By fostering interdisciplinary collaborations and conducting thorough investigations, stakeholders can fully leverage the transformative power of DL to enhance patient care and refine therapeutic strategies. Ultimately, this promises to usher in a new era of personalized and optimized radiotherapy treatment for improved patient outcomes.
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Affiliation(s)
- Andrea Lastrucci
- Department of Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (A.L.); (Y.W.); (R.R.)
| | - Yannick Wandael
- Department of Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (A.L.); (Y.W.); (R.R.)
| | - Renzo Ricci
- Department of Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy; (A.L.); (Y.W.); (R.R.)
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5
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Martín-Noguerol T, López-Úbeda P, Luna A. AI in radiology: Legal responsibilities and the car paradox. Eur J Radiol 2024; 175:111462. [PMID: 38608500 DOI: 10.1016/j.ejrad.2024.111462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/14/2024]
Abstract
The integration of AI in radiology raises significant legal questions about responsibility for errors. Radiologists fear AI may introduce new legal challenges, despite its potential to enhance diagnostic accuracy. AI tools, even those approved by regulatory bodies like the FDA or CE, are not perfect, posing a risk of failure. The key issue is how AI is implemented: as a stand-alone diagnostic tool or as an aid to radiologists. The latter approach could reduce undesired side effects. However, it's unclear who should be held liable for AI failures, with potential candidates ranging from engineers and radiologists involved in AI development to companies and department heads who integrate these tools into clinical practice. The EU's AI Act, recognizing AI's risks, categorizes applications by risk level, with many radiology-related AI tools considered high risk. Legal precedents in autonomous vehicles offer some guidance on assigning responsibility. Yet, the existing legal challenges in radiology, such as diagnostic errors, persist. AI's potential to improve diagnostics raises questions about the legal implications of not using available AI tools. For instance, an AI tool improving the detection of pediatric fractures could reduce legal risks. This situation parallels innovations like car turn signals, where ignoring available safety enhancements could lead to legal problems. The debate underscores the need for further research and regulation to clarify AI's role in radiology, balancing innovation with legal and ethical considerations.
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Affiliation(s)
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Medica, Jaén, Spain.
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6
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Martín-Noguerol T, López-Úbeda P, Luna A. Imagine there is no paperwork… it's easy if you try. Br J Radiol 2024; 97:744-746. [PMID: 38335929 PMCID: PMC11027242 DOI: 10.1093/bjr/tqae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/11/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.
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Affiliation(s)
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT medica, Jaén 23007, Spain
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7
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Kim D, Sundling KE, Virk R, Thrall MJ, Alperstein S, Bui MM, Chen-Yost H, Donnelly AD, Lin O, Liu X, Madrigal E, Michelow P, Schmitt FC, Vielh PR, Zakowski MF, Parwani AV, Jenkins E, Siddiqui MT, Pantanowitz L, Li Z. Digital cytology part 2: artificial intelligence in cytology: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force. J Am Soc Cytopathol 2024; 13:97-110. [PMID: 38158317 DOI: 10.1016/j.jasc.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024]
Abstract
Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported.
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Affiliation(s)
- David Kim
- Department of Pathology & Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Kaitlin E Sundling
- The Wisconsin State Laboratory of Hygiene and Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | - Renu Virk
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Michael J Thrall
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Susan Alperstein
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Marilyn M Bui
- The Department of Pathology, Moffitt Cancer Center & Research Institute, Tampa, Florida
| | | | - Amber D Donnelly
- Diagnostic Cytology Education, University of Nebraska Medical Center, College of Allied Health Professions, Omaha, Nebraska
| | - Oscar Lin
- Department of Pathology & Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Xiaoying Liu
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire
| | - Emilio Madrigal
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Pamela Michelow
- Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa; Department of Pathology, National Health Laboratory Services, Johannesburg, South Africa
| | - Fernando C Schmitt
- Department of Pathology, Medical Faculty of Porto University, Porto, Portugal
| | - Philippe R Vielh
- Department of Pathology, Medipath and American Hospital of Paris, Paris, France
| | | | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | | | - Momin T Siddiqui
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
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8
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Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH. AI for Detection of Tuberculosis: Implications for Global Health. Radiol Artif Intell 2024; 6:e230327. [PMID: 38197795 PMCID: PMC10982823 DOI: 10.1148/ryai.230327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024]
Abstract
Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Eui Jin Hwang
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Won Gi Jeong
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Pierre-Marie David
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Matthew Arentz
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Morten Ruhwald
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Soon Ho Yoon
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
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9
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Appel JM. Artificial intelligence in medicine and the negative outcome penalty paradox. JOURNAL OF MEDICAL ETHICS 2024:jme-2023-109848. [PMID: 38290853 DOI: 10.1136/jme-2023-109848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 01/18/2024] [Indexed: 02/01/2024]
Abstract
Artificial intelligence (AI) holds considerable promise for transforming clinical diagnostics. While much has been written both about public attitudes toward the use of AI tools in medicine and about uncertainty regarding legal liability that may be delaying its adoption, the interface of these two issues has so far drawn less attention. However, understanding this interface is essential to determining how jury behaviour is likely to influence adoption of AI by physicians. One distinctive concern identified in this paper is a 'negative outcome penalty paradox' (NOPP) in which physicians risk being penalised by juries in cases with negative outcomes, whether they overrule AI determinations or accept them. The paper notes three reasons why AI in medicine is uniquely susceptible to the NOPP and urges serious further consideration of this complex dilemma.
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Affiliation(s)
- Jacob M Appel
- Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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10
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Mahmood U, Shukla-Dave A, Chan HP, Drukker K, Samala RK, Chen Q, Vergara D, Greenspan H, Petrick N, Sahiner B, Huo Z, Summers RM, Cha KH, Tourassi G, Deserno TM, Grizzard KT, Näppi JJ, Yoshida H, Regge D, Mazurchuk R, Suzuki K, Morra L, Huisman H, Armato SG, Hadjiiski L. Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing. BJR ARTIFICIAL INTELLIGENCE 2024; 1:ubae003. [PMID: 38476957 PMCID: PMC10928809 DOI: 10.1093/bjrai/ubae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 03/14/2024]
Abstract
The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.
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Affiliation(s)
- Usman Mahmood
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, IL, 60637, United States
| | - Ravi K Samala
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Quan Chen
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, 85054, United States
| | - Daniel Vergara
- Department of Radiology, University of Washington, Seattle, WA, 98195, United States
| | - Hayit Greenspan
- Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mt Sinai, New York, NY, 10029, United States
| | - Nicholas Petrick
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Berkman Sahiner
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Zhimin Huo
- Tencent America, Palo Alto, CA, 94306, United States
| | - Ronald M Summers
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, United States
| | - Kenny H Cha
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Georgia Tourassi
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, United States
| | - Thomas M Deserno
- Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Braunschweig, Niedersachsen, 38106, Germany
| | - Kevin T Grizzard
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, 06510, United States
| | - Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, 10060, Italy
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, 56126, Italy
| | - Richard Mazurchuk
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, United States
| | - Kenji Suzuki
- Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan
| | - Lia Morra
- Department of Control and Computer Engineering, Politecnico di Torino, Torino, Piemonte, 10129, Italy
| | - Henkjan Huisman
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Gelderland, 6525 GA, Netherlands
| | - Samuel G Armato
- Department of Radiology, University of Chicago, Chicago, IL, 60637, United States
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, United States
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11
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Evans H, Snead D. Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them? Histopathology 2024; 84:279-287. [PMID: 37921030 DOI: 10.1111/his.15071] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 11/04/2023]
Abstract
Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.
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Affiliation(s)
- Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - David Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
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12
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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13
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Mello-Thoms C, Mello CAB. Clinical applications of artificial intelligence in radiology. Br J Radiol 2023; 96:20221031. [PMID: 37099398 PMCID: PMC10546456 DOI: 10.1259/bjr.20221031] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 04/27/2023] Open
Abstract
The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.
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Affiliation(s)
| | - Carlos A B Mello
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
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14
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Ahmed TM, Kawamoto S, Hruban RH, Fishman EK, Soyer P, Chu LC. A primer on artificial intelligence in pancreatic imaging. Diagn Interv Imaging 2023; 104:435-447. [PMID: 36967355 DOI: 10.1016/j.diii.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Sol Goldman Pancreatic Research Center, Department of Pathology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Department of Radiology, Hôpital Cochin-APHP, 75014, 75006, Paris, France, 7501475006
| | - Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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15
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Romeo V, Cavaliere C. Editorial: Use of DCE-MRI in female affecting cancers. Front Oncol 2023; 13:1260469. [PMID: 37637044 PMCID: PMC10456856 DOI: 10.3389/fonc.2023.1260469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
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16
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Laborie LB, Naidoo J, Pace E, Ciet P, Eade C, Wagner MW, Huisman TAGM, Shelmerdine SC. European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age. Pediatr Radiol 2023; 53:576-580. [PMID: 35731260 PMCID: PMC9214669 DOI: 10.1007/s00247-022-05426-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/26/2022] [Accepted: 06/03/2022] [Indexed: 11/08/2022]
Abstract
A new task force dedicated to artificial intelligence (AI) with respect to paediatric radiology was created in 2021 at the International Paediatric Radiology (IPR) meeting in Rome, Italy (a joint society meeting by the European Society of Pediatric Radiology [ESPR] and the Society for Pediatric Radiology [SPR]). The concept of a separate task force dedicated to AI was borne from an ESPR-led international survey of health care professionals' opinions, expectations and concerns regarding AI integration within children's imaging departments. In this survey, the majority (> 80%) of ESPR respondents supported the creation of a task force and helped define our key objectives. These include providing educational content about AI relevant for paediatric radiologists, brainstorming ideas for future projects and collaborating on AI-related studies with respect to collating data sets, de-identifying images and engaging in multi-case, multi-reader studies. This manuscript outlines the starting point of the ESPR AI task force and where we wish to go.
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Affiliation(s)
- Lene Bjerke Laborie
- grid.412008.f0000 0000 9753 1393Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway
- grid.7914.b0000 0004 1936 7443Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Jaishree Naidoo
- Paediatric Diagnostic Imaging and Envisionit Deep AI, Johannesburg, South Africa
| | - Erika Pace
- grid.5072.00000 0001 0304 893XDepartment of Diagnostic Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Pierluigi Ciet
- grid.5645.2000000040459992XDepartment of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- grid.5645.2000000040459992XDepartment of Pediatric Pulmonology and Allergology, Erasmus MC, Sophia’s Children’s Hospital, Rotterdam, The Netherlands
| | - Christine Eade
- grid.8391.30000 0004 1936 8024University of Exeter Medical School, Exeter, UK
| | - Matthias W. Wagner
- grid.42327.300000 0004 0473 9646Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for Sick Children, Toronto, Canada
- grid.17063.330000 0001 2157 2938Department of Medical Imaging, University of Toronto, Toronto, Ontario Canada
| | - Thierry A. G. M. Huisman
- grid.39382.330000 0001 2160 926XEdward B. Singleton Department of Radiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas USA
| | - Susan C. Shelmerdine
- grid.424537.30000 0004 5902 9895Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, WC1H 3JH London, UK
- grid.83440.3b0000000121901201UCL Great Ormond Street Institute of Child Health, London, UK
- grid.451056.30000 0001 2116 3923NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
- grid.464688.00000 0001 2300 7844Department of Clinical Radiology, St. George’s Hospital, London, UK
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17
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Khan A, Khunte M, Wu X, Bajaj S, Payabvash S, Wintermark M, Matouk C, Seidenwurm DJ, Gandhi D, Parizel P, Mezrich J, Malhotra A. Malpractice Litigation Related to Diagnosis and Treatment of Intracranial Aneurysms. AJNR Am J Neuroradiol 2023; 44:460-466. [PMID: 36997286 PMCID: PMC10084911 DOI: 10.3174/ajnr.a7828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/23/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND AND PURPOSE Approaches to management of intracranial aneurysms are inconsistent, in part due to apprehension relating to potential malpractice claims. The purpose of this article was to review the causes of action underlying medical malpractice lawsuits related to the diagnosis and management of intracranial aneurysms and to identify the factors associated and their outcomes. MATERIALS AND METHODS We consulted 2 large legal databases in the United States to search for cases in which there were jury awards and settlements related to the diagnosis and management of patients with intracranial aneurysms in the United States. Files were screened to include only those cases in which the cause of action involved negligence in the diagnosis and management of a patient with an intracranial aneurysm. RESULTS Between 2000 and 2020, two hundred eighty-seven published case summaries were identified, of which 133 were eligible for inclusion in the analysis. Radiologists constituted 16% of 159 physicians sued in these lawsuits. Failure to diagnose was the most common medical malpractice claim referenced (100/133 cases), with the most common subgroups being "failure to include cerebral aneurysm as a differential and thus perform adequate work-up" (30 cases), and "failure to correctly interpret aneurysm evidence on CT or MR imaging" (16 cases). Only 6 of these 16 cases were adjudicated at trial, with 2 decided in favor of the plaintiff (awarded $4,000,000 and $43,000,000, respectively). CONCLUSIONS Incorrect interpretation of imaging is relatively infrequent as a cause of malpractice litigation compared with failure to diagnose aneurysms in the clinical setting by neurosurgeons, emergency physicians, and primary care providers.
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Affiliation(s)
- A Khan
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
| | - M Khunte
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
| | - X Wu
- Department of Radiology (X.W.), University of California at San Francisco, San Francisco, California
| | - S Bajaj
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
| | - S Payabvash
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
| | - M Wintermark
- Department of Radiology (M.W.), MD Anderson Cancer Center, Houston, Texas
| | - C Matouk
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
- Neurosurgery (C.M.), Yale School of Medicine, New Haven, Connecticut
| | - D J Seidenwurm
- Department of Neuroradiology (D.J.S.), Sutter Health, Sacramento, California
| | - D Gandhi
- Departments of Interventional Neuroradiology, Radiology, and Nuclear Medicine (D.G.)
- Neurology (D.G.)
- Neurosurgery (D.G.), University of Maryland School of Medicine, Baltimore, Maryland
| | - P Parizel
- Department of Radiology (P.P.), University of Western Australia, Perth, Australia
| | - J Mezrich
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
| | - A Malhotra
- From the Departments of Radiology and Biomedical Imaging (A.K., M.K., S.B., S.P., C.M., J.M., A.M.)
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18
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Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI). Eur Radiol 2023:10.1007/s00330-023-09409-2. [PMID: 36729173 PMCID: PMC9892666 DOI: 10.1007/s00330-023-09409-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 11/29/2022] [Accepted: 12/27/2022] [Indexed: 02/03/2023]
Abstract
This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.
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19
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Bahl M. Artificial Intelligence in Clinical Practice: Implementation Considerations and Barriers. JOURNAL OF BREAST IMAGING 2022; 4:632-639. [PMID: 36530476 PMCID: PMC9741727 DOI: 10.1093/jbi/wbac065] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Indexed: 09/06/2023]
Abstract
The rapid growth of artificial intelligence (AI) in radiology has led to Food and Drug Administration clearance of more than 20 AI algorithms for breast imaging. The steps involved in the clinical implementation of an AI product include identifying all stakeholders, selecting the appropriate product to purchase, evaluating it with a local data set, integrating it into the workflow, and monitoring its performance over time. Despite the potential benefits of improved quality and increased efficiency with AI, several barriers, such as high costs and liability concerns, may limit its widespread implementation. This article lists currently available AI products for breast imaging, describes the key elements of clinical implementation, and discusses barriers to clinical implementation.
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Affiliation(s)
- Manisha Bahl
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
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20
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Giansanti D. The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews. Healthcare (Basel) 2022; 10:1824. [PMID: 36292270 PMCID: PMC9601605 DOI: 10.3390/healthcare10101824] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 09/05/2023] Open
Abstract
Today, there is growing interest in artificial intelligence (AI) in the field of digital radiology (DR). This is also due to the push that has been applied in this sector due to the pandemic. Many studies are devoted to the challenges of integration in the health domain. One of the most important challenges is that of regulations. This study conducted a narrative review of reviews on the international approach to the regulation of AI in DR. The design of the study was based on: (I) An overview on Scopus and Pubmed (II) A qualification and eligibility process based on a standardized checklist and a scoring system. The results have highlighted an international approach to the regulation of these systems classified as "software as medical devices (SaMD)" arranged into: ethical issues, international regulatory framework, and bottlenecks of the legal issues. Several recommendations emerge from the analysis. They are all based on fundamental pillars: (a) The need to overcome a differentiated approach between countries. (b) The need for greater transparency and publicity of information both for SaMDs as a whole and for the algorithms and test patterns. (c) The need for an interdisciplinary approach that avoids bias (including demographic) in algorithms and test data. (d) The need to reduce some limits/gaps of the scientific literature production that do not cover the international approach.
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21
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Soyer P, Fishman EK, Rowe SP, Patlas MN, Chassagnon G. Does artificial intelligence surpass the radiologist? Diagn Interv Imaging 2022; 103:445-447. [PMID: 35973913 DOI: 10.1016/j.diii.2022.08.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 12/30/2022]
Affiliation(s)
- Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Michael N Patlas
- Department of Radiology, Hamilton General Hospital, McMaster University Hamilton, ON, Canada L8L 2X2
| | - Guillaume Chassagnon
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
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22
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Mansour S, Soliman S, Kansakar A, Marey A, Hunold C, Hanafy MM. Strengths and challenges of the artificial intelligence in the assessment of dense breasts. BJR Open 2022; 4:20220018. [PMID: 38525169 PMCID: PMC10958665 DOI: 10.1259/bjro.20220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/27/2022] [Accepted: 07/20/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. Methods This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Results Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms. Conclusion Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Advances in knowledge Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.
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Affiliation(s)
| | - Somia Soliman
- Pathology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Abisha Kansakar
- Radiology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Ahmed Marey
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
| | - Christiane Hunold
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
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23
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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