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Tikhomirov L, Semmler C, McCradden M, Searston R, Ghassemi M, Oakden-Rayner L. Medical artificial intelligence for clinicians: the lost cognitive perspective. Lancet Digit Health 2024; 6:e589-e594. [PMID: 39059890 DOI: 10.1016/s2589-7500(24)00095-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/08/2024] [Accepted: 05/01/2024] [Indexed: 07/28/2024]
Abstract
The development and commercialisation of medical decision systems based on artificial intelligence (AI) far outpaces our understanding of their value for clinicians. Although applicable across many forms of medicine, we focus on characterising the diagnostic decisions of radiologists through the concept of ecologically bounded reasoning, review the differences between clinician decision making and medical AI model decision making, and reveal how these differences pose fundamental challenges for integrating AI into radiology. We argue that clinicians are contextually motivated, mentally resourceful decision makers, whereas AI models are contextually stripped, correlational decision makers, and discuss misconceptions about clinician-AI interaction stemming from this misalignment of capabilities. We outline how future research on clinician-AI interaction could better address the cognitive considerations of decision making and be used to enhance the safety and usability of AI models in high-risk medical decision-making contexts.
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Affiliation(s)
- Lana Tikhomirov
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia.
| | - Carolyn Semmler
- School of Psychology, University of Adelaide, Adelaide, SA, Australia
| | - Melissa McCradden
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Rachel Searston
- School of Psychology, University of Adelaide, Adelaide, SA, Australia
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science and Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
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2
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Ramaswamy T, Sparling JL, Chang MG, Bittner EA. Ten misconceptions regarding decision-making in critical care. World J Crit Care Med 2024; 13:89644. [PMID: 38855268 PMCID: PMC11155500 DOI: 10.5492/wjccm.v13.i2.89644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/25/2024] [Accepted: 03/01/2024] [Indexed: 06/03/2024] Open
Abstract
Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.
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Affiliation(s)
- Tara Ramaswamy
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Jamie L Sparling
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Marvin G Chang
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Edward A Bittner
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
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3
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Yu KH, Healey E, Leong TY, Kohane IS, Manrai AK. Medical Artificial Intelligence and Human Values. N Engl J Med 2024; 390:1895-1904. [PMID: 38810186 DOI: 10.1056/nejmra2214183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Affiliation(s)
- Kun-Hsing Yu
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Elizabeth Healey
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Tze-Yun Leong
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Isaac S Kohane
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Arjun K Manrai
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
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4
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Dolatkhah Laein G. Global perspectives on governing healthcare AI: prioritising safety, equity and collaboration. BMJ LEADER 2024:leader-2023-000904. [PMID: 38806230 DOI: 10.1136/leader-2023-000904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 05/14/2024] [Indexed: 05/30/2024]
Affiliation(s)
- Ghasem Dolatkhah Laein
- Mashhad University of Medical Sciences, Mashhad, Razavi Khorasan, Iran (the Islamic Republic of)
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5
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Amiri H, Peiravi S, Rezazadeh Shojaee SS, Rouhparvarzamin M, Nateghi MN, Etemadi MH, ShojaeiBaghini M, Musaie F, Anvari MH, Asadi Anar M. Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC MEDICAL EDUCATION 2024; 24:412. [PMID: 38622577 PMCID: PMC11017500 DOI: 10.1186/s12909-024-05406-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Nowadays, Artificial intelligence (AI) is one of the most popular topics that can be integrated into healthcare activities. Currently, AI is used in specialized fields such as radiology, pathology, and ophthalmology. Despite the advantages of AI, the fear of human labor being replaced by this technology makes some students reluctant to choose specific fields. This meta-analysis aims to investigate the knowledge and attitude of medical, dental, and nursing students and experts in this field about AI and its application. METHOD This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis. RESULT Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P < 0.01, I2 = 98.95%) for knowledge. Moreover, the proportion of attitude was 0.65 (95%CI = [0.55, 0.75], P < 0.01, I2 = 99.47%). The studies did not show any publication bias with a symmetrical funnel plot. CONCLUSION Average levels of knowledge indicate the necessity of including relevant educational programs in the student's academic curriculum. The positive attitude of students promises the acceptance of AI technology. However, dealing with ethics education in AI and the aspects of human-AI cooperation are discussed. Future longitudinal studies could follow students to provide more data to guide how AI can be incorporated into education.
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Affiliation(s)
- Hamidreza Amiri
- Student Research Committee, Arak University of Medical Sciences, Arak, Iran
| | - Samira Peiravi
- Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyedeh Sara Rezazadeh Shojaee
- Department of Nursing, Faculty of Nursing and Midwifery, Mashhad Medical Sciences, Islamic Azad University, Mashhad, Iran
| | - Motahareh Rouhparvarzamin
- Student Research Committee, School of Nursing and Midwifery, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mohammad Naser Nateghi
- Student Research Committee, Faculty of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Hossein Etemadi
- Students Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahdie ShojaeiBaghini
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Farhan Musaie
- Dentistry Student, Dental Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Hossein Anvari
- Master of Health Science, Faculty of Health Sciences, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Mahsa Asadi Anar
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, SBUMS, Arabi Ave, Daneshjoo Blvd, Velenjak, Tehran, 19839-63113, Iran.
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Lakkimsetti M, Devella SG, Patel KB, Dhandibhotla S, Kaur J, Mathew M, Kataria J, Nallani M, Farwa UE, Patel T, Egbujo UC, Meenashi Sundaram D, Kenawy S, Roy M, Khan SF. Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature. Cureus 2024; 16:e58400. [PMID: 38756258 PMCID: PMC11098056 DOI: 10.7759/cureus.58400] [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: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.
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Affiliation(s)
| | - Swati G Devella
- Medicine, Kempegowda Institute of Medical Sciences, Bangalore, IND
| | - Keval B Patel
- Surgery, Narendra Modi Medical College, Ahmedabad, IND
| | | | | | - Midhun Mathew
- Internal Medicine, Trinitas Regional Medical Center, Elizabeth, USA
| | | | - Manisha Nallani
- Medicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND
| | - Umm E Farwa
- Emergency Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Tirath Patel
- Medicine, American University of Antigua, Saint John's, ATG
| | | | - Dakshin Meenashi Sundaram
- Internal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND
| | | | - Mehak Roy
- Internal Medicine, School of Medicine Science and Research, Delhi, IND
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7
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Giannitto C, Carnicelli G, Lusi S, Ammirabile A, Casiraghi E, De Virgilio A, Esposito AA, Farina D, Ferreli F, Franzese C, Frigerio GM, Lo Casto A, Malvezzi L, Lorini L, Othman AE, Preda L, Scorsetti M, Bossi P, Mercante G, Spriano G, Balzarini L, Francone M. The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey. J Pers Med 2024; 14:341. [PMID: 38672968 PMCID: PMC11050769 DOI: 10.3390/jpm14040341] [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: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.
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Affiliation(s)
- Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giorgia Carnicelli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Stefano Lusi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni”, University of Milan, Via Celoria 18, 20133 Milan, Italy;
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 717 Potter Street, Berkeley, CA 94710, USA
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | | | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Fabio Ferreli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Gian Marco Frigerio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Antonio Lo Casto
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, 90127 Palermo, Italy;
| | - Luca Malvezzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luigi Lorini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Medical Oncology and Hematology Unit IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ahmed E. Othman
- Department of Neuroradiology, University Medical Center Mainz, 55131 Mainz, Germany;
| | - Lorenzo Preda
- Radiology Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luca Balzarini
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
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Veritti D, Rubinato L, Sarao V, De Nardin A, Foresti GL, Lanzetta P. Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology. Graefes Arch Clin Exp Ophthalmol 2024; 262:975-982. [PMID: 37747539 PMCID: PMC10907411 DOI: 10.1007/s00417-023-06245-4] [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: 01/19/2023] [Revised: 07/24/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023] Open
Abstract
PURPOSE This narrative review aims to provide an overview of the dangers, controversial aspects, and implications of artificial intelligence (AI) use in ophthalmology and other medical-related fields. METHODS We conducted a decade-long comprehensive search (January 2013-May 2023) of both academic and grey literature, focusing on the application of AI in ophthalmology and healthcare. This search included key web-based academic databases, non-traditional sources, and targeted searches of specific organizations and institutions. We reviewed and selected documents for relevance to AI, healthcare, ethics, and guidelines, aiming for a critical analysis of ethical, moral, and legal implications of AI in healthcare. RESULTS Six main issues were identified, analyzed, and discussed. These include bias and clinical safety, cybersecurity, health data and AI algorithm ownership, the "black-box" problem, medical liability, and the risk of widening inequality in healthcare. CONCLUSION Solutions to address these issues include collecting high-quality data of the target population, incorporating stronger security measures, using explainable AI algorithms and ensemble methods, and making AI-based solutions accessible to everyone. With careful oversight and regulation, AI-based systems can be used to supplement physician decision-making and improve patient care and outcomes.
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Affiliation(s)
- Daniele Veritti
- Department of Medicine - Ophthalmology, University of Udine, Udine, Italy.
| | - Leopoldo Rubinato
- Department of Medicine - Ophthalmology, University of Udine, Udine, Italy
| | - Valentina Sarao
- Department of Medicine - Ophthalmology, University of Udine, Udine, Italy
- Istituto Europeo di Microchirurgia Oculare - IEMO, Udine, Italy
| | - Axel De Nardin
- Department of Mathematics, Informatics and Physics, University of Udine, Udine, Italy
| | - Gian Luca Foresti
- Department of Mathematics, Informatics and Physics, University of Udine, Udine, Italy
| | - Paolo Lanzetta
- Department of Medicine - Ophthalmology, University of Udine, Udine, Italy
- Istituto Europeo di Microchirurgia Oculare - IEMO, Udine, Italy
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9
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Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark 2024:CBM230360. [PMID: 38427470 DOI: 10.3233/cbm-230360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.
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10
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Allen MR, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G. Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC PRIMARY CARE 2024; 25:42. [PMID: 38281026 PMCID: PMC10821550 DOI: 10.1186/s12875-024-02282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms. METHODS From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents. RESULTS Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship). CONCLUSION Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.
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Affiliation(s)
- Matthew R Allen
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Sophie Webb
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ammar Mandvi
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Marshall Frieden
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ming Tai-Seale
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gene Kallenberg
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
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11
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Terranova C, Cestonaro C, Fava L, Cinquetti A. AI and professional liability assessment in healthcare. A revolution in legal medicine? Front Med (Lausanne) 2024; 10:1337335. [PMID: 38259835 PMCID: PMC10800912 DOI: 10.3389/fmed.2023.1337335] [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: 11/12/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial intelligence may enhance patient safety and improve healthcare outcomes, but it presents notable ethical and legal dilemmas. Moreover, as AI streamlines the analysis of the multitude of factors relevant to malpractice claims, including informed consent, adherence to standards of care, and causation, the evaluation of professional liability might also benefit from its use. Beginning with an analysis of the basic steps in assessing professional liability, this article examines the potential new medical-legal issues that an expert witness may encounter when analyzing malpractice cases and the potential integration of AI in this context. These changes related to the use of integrated AI, will necessitate efforts on the part of judges, experts, and clinicians, and may require new legislative regulations. A new expert witness will be likely necessary in the evaluation of professional liability cases. On the one hand, artificial intelligence will support the expert witness; however, on the other hand, it will introduce specific elements into the activities of healthcare workers. These elements will necessitate an expert witness with a specialized cultural background. Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool. The combination of AI with human judgment in these assessments can enhance comprehensiveness and fairness. However, it is imperative to adopt a cautious and balanced approach to prevent complete automation in this field.
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Affiliation(s)
- Claudio Terranova
- Legal Medicine and Toxicology, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua, Italy
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Baby D, John L, Pia JC, Sreedevi PV, Pattnaik SJ, Varkey A, Gupta S. Role of robotics and artificial intelligence in oral health education. Knowledge, perception and attitude of dentists in India. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:384. [PMID: 38333180 PMCID: PMC10852163 DOI: 10.4103/jehp.jehp_379_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/22/2023] [Indexed: 02/10/2024]
Abstract
BACKGROUND Artificial intelligence or AI may be identified as the use of Personal Computers and/or machines in performing certain specific types of tasks that usually have the requirement of humanized knowledge. These specific tasks include acknowledgment of the problem, understanding disease dynamics, and determining the clinical diagnosis. MATERIALS AND METHODS This cross-sectional and prospective study was conducted on Dental professionals who were practicing all across India after obtaining approval from the Institutional Ethical Board. A previously validated as well as pre-analyzed questionnaire form was distributed using electronic mail and through the use of social media with a briefly explained description of the study purpose and an informed consent form. The study questionnaire comprised "close-ended" queries that were then divided into foursections. All the study participants were then instructed to select any one option among all the provided answers. The entire study was completed within one month. Collected observations were entered within a Microsoft Excel 2007® master chart. Statistical analytical software tool SPSS version 20.0, IBM Corporation was employed. "Chi-square" test was performed for evaluating statistical association. A P value lesser than 0.05 was fixed with statistical significance. RESULTS On analyzing the level of knowledge, 82.5% of subjects had knowledge of artificial intelligence while 11.4% had no knowledge and 6.1% had some knowledge of this tool. 69.1% were knowledgeable regarding the use of AI in lesional diagnosis, 12.8% had no knowledge regarding artificial intelligence for the diagnosis and 18.1% had no knowledge regarding AI in the diagnosis. 71% had knowledge concerning the use of AI for Imaging. Knowledge of AI in Oral Hygiene was seen in 54.3%. 91.2% of participants had knowledge of robotics use in Oral Surgery. 77% of dentists had knowledge regarding the use of AI for the enhancement of clinical practice. 95.5% had a higher 'positive' attitude toward the use of AI in academics. 69.1% of dentists had a positive attitude regarding AI incorporation in practice. 5% of dentists considered artificial intelligence better than human intelligence for diagnosis. 10% believed that disparities can exist between AI-based and human diagnosis. CONCLUSION Positive correlations were noted between knowledge, attitude, and practice of AI among studied dentists.
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Affiliation(s)
- Deepak Baby
- Department of Conservative Dentistry and Endodontics, P.S.M. Dental College and Research Center, Thrissur, Kerala, India
| | - Lauabel John
- Department of Conservative Dentistry and Endodontics, P.S.M. Dental College and Research Center, Thrissur, Kerala, India
| | - Joseph Changankary Pia
- Department of Conservative Dentistry and Endodontics, P.S.M. Dental College and Research Center, Thrissur, Kerala, India
| | - PV Sreedevi
- Department of Conservative Dentistry and Endodontics, P.S.M. Dental College and Research Center, Thrissur, Kerala, India
| | - Samarjeet J. Pattnaik
- Department of Periodontics, Hitech Dental College and Hospital, Bhubaneswar, Odisha, India
| | - Anish Varkey
- Department of Periodontics, KMCT Dental College, Kozhikode, Kerala, India
| | - Shivam Gupta
- Private Dental Practitioner, New Delhi, Delhi, India
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Mallio CA, Radbruch A, Deike-Hofmann K, van der Molen AJ, Dekkers IA, Zaharchuk G, Parizel PM, Beomonte Zobel B, Quattrocchi CC. Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI: A Literature Review. Invest Radiol 2023; 58:746-753. [PMID: 37126454 DOI: 10.1097/rli.0000000000000983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
ABSTRACT Brain and cardiac MRIs are fundamental noninvasive imaging tools, which can provide important clinical information and can be performed without or with gadolinium-based contrast agents (GBCAs), depending on the clinical indication. It is currently a topic of debate whether it would be feasible to extract information such as standard gadolinium-enhanced MRI while injecting either less or no GBCAs. Artificial intelligence (AI) is a great source of innovation in medical imaging and has been explored as a method to synthesize virtual contrast MR images, potentially yielding similar diagnostic performance without the need to administer GBCAs. If possible, there would be significant benefits, including reduction of costs, acquisition time, and environmental impact with respect to conventional contrast-enhanced MRI examinations. Given its promise, we believe additional research is needed to increase the evidence to make these AI solutions feasible, reliable, and robust enough to be integrated into the clinical framework. Here, we review recent AI studies aimed at reducing or replacing gadolinium in brain and cardiac imaging while maintaining diagnostic image quality.
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Affiliation(s)
| | - Alexander Radbruch
- Clinic for Diagnostic and Interventional Neuroradiology, University Clinic Bonn, and German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Katerina Deike-Hofmann
- Clinic for Diagnostic and Interventional Neuroradiology, University Clinic Bonn, and German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Aart J van der Molen
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ilona A Dekkers
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA
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Kalaimani G, B S, Chockalingam RM, Karthick P. Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence Among Dentists and Dental Students: A Cross-Sectional Online Survey. Cureus 2023; 15:e44656. [PMID: 37799215 PMCID: PMC10549783 DOI: 10.7759/cureus.44656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is the process by which it is possible to program computers to mimic human thoughts. AI and its subsets machine learning and deep learning have been developed to analyze complicated data gathered from many sources using algorithms built into decision support systems. It has been widely used in the field of dentistry. AIM The study aimed to evaluate the knowledge, attitude, and practice (KAP) of AI among dental students and dentists. METHODOLOGY The present study is a descriptive cross-sectional online survey that was carried out among dentists and dental students in South India. A self-structured, close-ended questionnaire that was administered that consisted of 25 questions was included. The questions were circulated through Google Forms (Google LLC, Mountain View, California, United States), and it was circulated among the study participants through online mode. The data were collected systematically, and SPSS Statistics version 22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.) was used for data analysis. RESULTS One thousand (595 dental surgeons and 405 dental students) participated in the study through Google Forms. Among these, 700 (70%) were females and 300 (30%) were males. In the study group, 635 (63.5%) were aware of AI, and 365 (36.5%) were not aware (p-value 0.000). Among the 21 questions used to assess the KAP, 14 questions were significant with a p-value less than 0.05. More than 60% agreed that the dental curriculum has to be updated with AI. About 269 (26.9%) agreed that AI will replace the role of dentists in the future. There were no significant results in comparing dental surgeons and dental students. CONCLUSION The present study showed that the KAP among dental surgeons and dental students was the same. They believe that the dental curriculum has to be updated with AI. This study shows that there is a lack of knowledge about deep learning models and websites used for AI among dentists. Thus, it is necessary to include evidence-based teaching and training about the application of AI in dental practice to improve the future of dentistry.
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Affiliation(s)
| | - Sivapathasundharam B
- Oral Pathology and Microbiology, Priyadarshini Dental College and Hospital, Chennai, IND
| | | | - Prem Karthick
- Oral Pathology, Priyadarshini Dental College and Hospital, Chennai, IND
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Gniadek T, Kang J, Theparee T, Krive J. Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine. Online J Public Health Inform 2023; 15:e50934. [PMID: 38046562 PMCID: PMC10689048 DOI: 10.2196/50934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 02/27/2023] [Indexed: 12/05/2023] Open
Abstract
Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm's clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.
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Affiliation(s)
- Thomas Gniadek
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Jason Kang
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Talent Theparee
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Jacob Krive
- Department of Biomedical and Health Information Sciences University of Illinois at Chicago Chicago, IL United States
- Department of Health Information Technology NorthShore University Health System Evanston, IL United States
- Department of Health Informatics Dr Kiran C Patel School of Osteopathic Medicine Nova Southeastern University Fort Lauderdale, FL United States
- Pritzker School of Medicine University of Chicago Chicago, IL United States
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16
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Boulitsakis Logothetis S, Green D, Holland M, Al Moubayed N. Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making. Sci Rep 2023; 13:13563. [PMID: 37604974 PMCID: PMC10442440 DOI: 10.1038/s41598-023-40661-0] [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: 12/09/2022] [Accepted: 08/16/2023] [Indexed: 08/23/2023] Open
Abstract
The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.
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Affiliation(s)
| | - Darren Green
- Department of Renal Medicine, Northern Care Alliance NHS Foundation Trust, Manchester, UK
- Division of Cardiovascular Sciences, University of Manchester, Manchester, UK
| | - Mark Holland
- School of Clinical and Biomedical Sciences, University of Bolton, Bolton, UK
| | - Noura Al Moubayed
- Department of Computer Science, University of Durham, Durham, UK.
- Evergreen Life Ltd, Manchester, UK.
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17
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Planells H, Parmar V, Marcus HJ, Pandit AS. From theory to practice: what is the potential of artificial intelligence in the future of neurosurgery? Expert Rev Neurother 2023; 23:1041-1046. [PMID: 37997765 DOI: 10.1080/14737175.2023.2285432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Affiliation(s)
- Hannah Planells
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Viraj Parmar
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Anand S Pandit
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- High-dimensional Neurology, Institute of Neurology, London, UK
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18
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Fujihara K, Yamada Harada M, Horikawa C, Iwanaga M, Tanaka H, Nomura H, Sui Y, Tanabe K, Yamada T, Kodama S, Kato K, Sone H. Machine learning approach to predict body weight in adults. Front Public Health 2023; 11:1090146. [PMID: 37397751 PMCID: PMC10308016 DOI: 10.3389/fpubh.2023.1090146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/10/2023] [Indexed: 07/04/2023] Open
Abstract
Background Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings. Methods We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression. Results The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m2) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m2). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (p = 0.323). Conclusion The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.
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Affiliation(s)
- Kazuya Fujihara
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Mayuko Yamada Harada
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan
| | - Midori Iwanaga
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | | | | | | | | | - Takaho Yamada
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Satoru Kodama
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
| | - Kiminori Kato
- Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University, Niigata, Japan
| | - Hirohito Sone
- Department of Endocrinology and Metabolism, Faculty of Medicine, Niigata University, Niigata, Japan
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James KN, Phadke S, Wong TC, Chowdhury S. Artificial Intelligence in the Genetic Diagnosis of Rare Disease. Clin Lab Med 2023; 43:127-143. [PMID: 36764805 DOI: 10.1016/j.cll.2022.09.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Kiely N James
- Genomics, Rady Children's Institute for Genomic Medicine, 7910 Frost Street, MC5129, San Diego, CA 92123, USA
| | - Sujal Phadke
- Genomics, Rady Children's Institute for Genomic Medicine, 7910 Frost Street, MC5129, San Diego, CA 92123, USA
| | - Terence C Wong
- Genomics, Rady Children's Institute for Genomic Medicine, 7910 Frost Street, MC5129, San Diego, CA 92123, USA
| | - Shimul Chowdhury
- Rady Children's Institute for Genomic Medicine, 7910 Frost Street, MC5129, San Diego, CA 92123, USA.
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Russell RG, Lovett Novak L, Patel M, Garvey KV, Craig KJT, Jackson GP, Moore D, Miller BM. Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2023; 98:348-356. [PMID: 36731054 DOI: 10.1097/acm.0000000000004963] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
PURPOSE The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. METHOD In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team. RESULTS Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care. CONCLUSIONS The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.
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Affiliation(s)
- Regina G Russell
- R.G. Russell is director of learning system outcomes, Office of Undergraduate Medical Education, and assistant professor of medical education and administration, Vanderbilt University School of Medicine, Nashville Tennessee; ORCID: https://orcid.org/0000-0002-5540-7073
| | - Laurie Lovett Novak
- L.L. Novak is director, Center of Excellence in Applied Artificial Intelligence, Vanderbilt University Medical Center, and associate professor of biomedical informatics, Vanderbilt University School of Medicine, Nashville, Tennessee; ORCID: https://orcid.org/0000-0002-0415-4301
| | - Mehool Patel
- M. Patel is associate chief health officer and chief medical officer of provider analytics, IBM Watson Health, Cambridge, Massachusetts, and clinical professor, Northeast Ohio Medical University, Rootstown, Ohio
| | - Kim V Garvey
- K.V. Garvey is research instructor in anesthesiology, Vanderbilt University School of Medicine, and director of operations, Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, Tennessee; ORCID: https://orcid.org/0000-0002-2427-0182
| | - Kelly Jean Thomas Craig
- K.J.T. Craig is lead director, Clinical Evidence Development, Aetna Medical Affairs, CVS Health. At the time this work was completed, the author was deputy chief science officer of evidence-based practice, Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, Massachusetts; ORCID: https://orcid.org/0000-0002-9954-2795
| | - Gretchen P Jackson
- G.P. Jackson is vice president and scientific medical officer, Intuitive Surgical, Sunnyvale, California, and associate professor of surgery, pediatrics, and biomedical informatics, Vanderbilt University School of Medicine, Nashville, Tennessee. At the beginning of this work, the author was vice president and chief science officer, IBM Watson Health, Cambridge, Massachusetts; ORCID: https://orcid.org/0000-0002-3242-8058
| | - Don Moore
- D. Moore is emeritus professor of medical education and administration, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Bonnie M Miller
- B.M. Miller is professor of medical education and administration, Vanderbilt University School of Medicine, and director, Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, Tennessee; ORCID: https://orcid.org/0000-0002-7333-3389
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21
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Ameen S, Wong MC, Turner P, Yee KC. Improving colorectal cancer screening - consumer-centred technological interventions to enhance engagement and participation amongst diverse cohorts. Clin Res Hepatol Gastroenterol 2023; 47:102064. [PMID: 36494072 DOI: 10.1016/j.clinre.2022.102064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
The current "Gold Standard" colorectal cancer (CRC) screening approach of faecal occult blood test (FOBT) with follow-up colonoscopy has been shown to significantly improve morbidity and mortality, by enabling the early detection of disease. However, its efficacy is predicated on high levels of population participation in screening. Several international studies have shown continued low rates of screening participation, especially amongst highly vulnerable lower socio-economic cohorts, with minimal improvement using current recruitment strategies. Research suggests that a complex of dynamic factors (patient, clinician, and the broader health system) contribute to low citizen engagement. This paper argues that the challenges of screening participation can be better addressed by (1) developing dynamic multifaceted technological interventions collaboratively across stakeholders using human-centered design; (2) integrating consumer-centred artificial intelligence (AI) technologies to maximise ease of use for CRC screening; and (3) tailored strategies that maximise population screening engagement, especially amongst the most vulnerable.
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Affiliation(s)
- Saleem Ameen
- College of Health and Medicine, University of Tasmania, Hobart 7000, Tasmania, Australia.
| | - Ming Chao Wong
- College of Sciences and Engineering, University of Tasmania, Hobart 7000, Tasmania, Australia
| | - Paul Turner
- College of Sciences and Engineering, University of Tasmania, Hobart 7000, Tasmania, Australia
| | - Kwang Chien Yee
- College of Health and Medicine, University of Tasmania, Hobart 7000, Tasmania, Australia
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22
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Yoo J, Hur S, Hwang W, Cha WC. Healthcare Professionals' Expectations of Medical Artificial Intelligence and Strategies for its Clinical Implementation: A Qualitative Study. Healthc Inform Res 2023; 29:64-74. [PMID: 36792102 PMCID: PMC9932312 DOI: 10.4258/hir.2023.29.1.64] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 01/05/2023] [Indexed: 02/10/2023] Open
Abstract
OBJECTIVES Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully. METHODS Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement. RESULTS While the participants expressed expectations that medical AI could enhance their patients' outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment. CONCLUSIONS Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.
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Affiliation(s)
- Junsang Yoo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul,
Korea
| | - Sujeong Hur
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul,
Korea,AVOMD Inc, Seoul,
Korea
| | - Wonil Hwang
- Department of Industrial and Information Systems Engineering, Soongsil University, Seoul,
Korea
| | - Won Chul Cha
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul,
Korea,Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul,
Korea,Digital Innovation Center, Samsung Medical Center, Seoul,
Korea
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Ulloa M, Rothrock B, Ahmad FS, Jacobs M. Invisible clinical labor driving the successful integration of AI in healthcare. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.1045704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Artificial Intelligence and Machine Learning (AI/ML) tools are changing the landscape of healthcare decision-making. Vast amounts of data can lead to efficient triage and diagnosis of patients with the assistance of ML methodologies. However, more research has focused on the technological challenges of developing AI, rather than the system integration. As a result, clinical teams' role in developing and deploying these tools has been overlooked. We look to three case studies from our research to describe the often invisible work that clinical teams do in driving the successful integration of clinical AI tools. Namely, clinical teams support data labeling, identifying algorithmic errors and accounting for workflow exceptions, translating algorithmic output to clinical next steps in care, and developing team awareness of how the tool is used once deployed. We call for detailed and extensive documentation strategies (of clinical labor, workflows, and team structures) to ensure this labor is valued and to promote sharing of sociotechnical implementation strategies.
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Liu G, Zhao J, Tian G, Li S, Lu Y. Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis. Front Oncol 2022; 12:925924. [PMID: 36518311 PMCID: PMC9742812 DOI: 10.3389/fonc.2022.925924] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 11/11/2022] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND In recent years, the rapid development of artificial intelligence (AI) technology has created a new diagnostic and therapeutic opportunity for colorectal cancer (CRC). Numerous academic and clinical studies have demonstrated that high-level auxiliary diagnosis and treatment systems based on AI technology can significantly improve the readability of medical data, objectively provide a reliable and comprehensive reference for physicians, reduce the experience gap between physicians, and aid physicians in making more accurate diagnosis decisions. In this study, we used bibliometric techniques to visually analyze the literature about AI in the CRC field and summarize the current situation and research hotspots in this field. METHODS The relevant literature on AI in the field of CRC research was obtained from the Web of Science Core Collection (WoSCC) database. The software CiteSpace was utilized to analyze the number of papers, countries, institutions, authors, journals, cited literature, and keywords of the included literature and generate a visual knowledge map. The present study aims to evaluate the origin, current hotspots, and research trends of AI in CRC using bibliometric analysis. RESULTS As of March 2022, 64 nations/regions, 230 institutions, 245 journals, and 300 authors had published 562 AI-related articles in the field of CRC. Since 2016, each year has seen an exponential increase. China and the United States were the largest contributors, with the largest number of beneficial research institutions and the closest collaboration relationship. The World Journal of Gastroenterology is this field's most widely published journal. Diagnosis and treatment research, gene and immunology research, intestinal polyp research, tumor grading research, gastrointestinal endoscopy research, and prognosis research comprised the six topics derived from high-frequency keyword cluster analysis. CONCLUSION In recent years, field research has been a popular topic of discussion. The results of our bibliometric analysis allow us to comprehend better the current situation and trend of this research field, and the quantitative data indicators can serve as a guide for the research and application of global scholars.
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Affiliation(s)
- Guangwei Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
| | - Jun Zhao
- Department of Pharmacy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangye Tian
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Shuai Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Yun Lu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
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Zhang S, Wang Y, Zheng Q, Li J, Huang J, Long X. Artificial intelligence in melanoma: A systematic review. J Cosmet Dermatol 2022; 21:5993-6004. [PMID: 36001057 DOI: 10.1111/jocd.15323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Melanoma accounts for the majority of skin cancer deaths. Artificial intelligence has been applied in many types of cancers, and in melanoma in recent years. However, no systematic review summarized the application of artificial intelligence in melanoma. AIMS This study aims to systematically review previously published articles to explore the application of artificial intelligence in melanoma. MATERIALS & METHODS PubMed database was used to search the eligible publications on August 1, 2020. The query term was "artificial intelligence" and "melanoma." RESULTS A total of 51 articles were included in this review. Artificial intelligence technique is mainly used in the evaluation of dermoscopic images, other image segmentation and processing, and artificial intelligence diagnosis system. DISCUSSION Artificial intelligence is also applied in metastasis prediction, drug response prediction, and prognosis of melanoma. Besides, patients' perspectives of artificial intelligence and collaboration of human and artificial intelligence in melanoma also attracted attention. The query term might not include all articles, and we could not examine the algorithms that were built without publication. CONCLUSION The performance of artificial intelligence in melanoma is satisfactory and the future for potential applications is enormous.
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Affiliation(s)
- Shu Zhang
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yuanzhuo Wang
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Qingyue Zheng
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiarui Li
- Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiuzuo Huang
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiao Long
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Aboalshamat KT. Perception and Utilization of Artificial Intelligence (AI) among Dental Professionals in Saudi Arabia. Open Dent J 2022. [DOI: 10.2174/18742106-v16-e2208110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Objective:
Artificial intelligence (AI) is the new buzzword that is trendy in multiple branches of dentistry. The aim of this study was to assess perceptions and utilization of AI among dental professionals in Saudi Arabia.
Methods:
This was a cross-sectional study investigating 389 dental students and dentists from 22 cities in Saudi Arabia using a validated self-reported questionnaire.
Results:
A total of 49.4% of participants reported knowing what AI is; 44.5% reported having basic knowledge of AI principles, and 42.2% know of AI uses in dentistry. The most common AI information source was social media (66.07%). Out of 17 AI attitude items, 16 were scored above the midpoint. A total of 75.0% of participants agreed or strongly agreed AI will lead to major advances in dentistry. In contrast, 49.1% agreed or strongly agreed that AI could replace dentists in the future. There were no significant differences by gender or region, but students and interns had significantly higher attitude scores than did dentists. There was a widespread desire to take professional courses in dental AI use (69.7%), and some had used dental AI applications (25.4%) or taken an AI course (18.5%–20.3%). The most common barriers to dental AI use were non-availability of courses (73%) and lack of time (68.9%).
Conclusion:
Dental professionals in Saudi Arabia have moderate awareness levels and high rates of good attitudes about AI in dentistry. However, AI use in practice is limited. Incorporating AI in dental curricula is crucial due to the worldwide digital transformation.
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Meyer J, Khademi A, Têtu B, Han W, Nippak P, Remisch D. Impact of artificial intelligence on pathologists' decisions: an experiment. J Am Med Inform Assoc 2022; 29:1688-1695. [PMID: 35751441 DOI: 10.1093/jamia/ocac103] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/30/2022] [Accepted: 06/09/2022] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE The accuracy of artificial intelligence (AI) in medicine and in pathology in particular has made major progress but little is known on how much these algorithms will influence pathologists' decisions in practice. The objective of this paper is to determine the reliance of pathologists on AI and to investigate whether providing information on AI impacts this reliance. MATERIALS AND METHODS The experiment using an online survey design. Under 3 conditions, 116 pathologists and pathology students were tasked with assessing the Gleason grade for a series of 12 prostate biopsies: (1) without AI recommendations, (2) with AI recommendations, and (3) with AI recommendations accompanied by information about the algorithm itself, specifically algorithm accuracy rate and algorithm decision-making process. RESULTS Participant responses were significantly more accurate with the AI decision aids than without (92% vs 87%, odds ratio 13.30, P < .01). Unexpectedly, the provision of information on the algorithm made no significant difference compared to AI without information. The reliance on AI correlated with general beliefs on AI's usefulness but not with particular assessments of the AI tool offered. Decisions were made faster when AI was provided. DISCUSSION These results suggest that pathologists are willing to rely on AI regardless of accuracy or explanations. Generalization beyond the specific tasks and explanations provided will require further studies. CONCLUSION This study suggests that the factors that influence the reliance on AI differ in practice from beliefs expressed by clinicians in surveys. Implementation of AI in prospective settings should take individual behaviors into account.
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Affiliation(s)
- Julien Meyer
- School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada
| | - April Khademi
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
| | - Bernard Têtu
- Départment de biologie médicale, Université Laval, Québec City, Quebec, Canada
| | - Wencui Han
- Department of Business administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Pria Nippak
- School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada
| | - David Remisch
- School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada
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Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022; 28:1573-1583. [PMID: 35699597 PMCID: PMC9527612 DOI: 10.1093/ibd/izac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
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Affiliation(s)
| | | | - Enrico Mossotto
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Sarah Ennis
- Address correspondence to: Sarah Ennis, Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK ()
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Smits M, Nacar M, D S Ludden G, van Goor H. Stepwise Design and Evaluation of a Values-Oriented Ambient Intelligence Healthcare Monitoring Platform. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:914-923. [PMID: 35525831 DOI: 10.1016/j.jval.2021.11.1372] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/28/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES The majority of all developed digital health technologies do not reach successful implementation. A discrepancy among technology design, the context of use, and user needs and values is identified as the main reason for this failure. Value-sensitive design (VSD) is a design method enabling to align design with user values by embedding values in technology, yet the method is lacking clear heuristics for practical application. To improve the successful design and implementation of digital health, we propose and evaluate a stepwise approach to VSD. METHODS The approach consists of the phases: experiment, demonstrate, and validate. Experiment takes place in an office to create makeshift solutions. Demonstrate takes place in a mock-up environment and aims to optimize design requirements through user feedback. The validate phase takes place in an authentic care situation and studies how the novel technology affects current workflows. RESULTS We applied the stepwise VSD approach to the design of a hospital-based ambient intelligence solution for remotely and continuously monitoring quality and safety of patient care. We particularly focused on embodiment of the values of safety, privacy, and inclusiveness in the design. Design activities of the experiment and demonstrate phase are discussed. CONCLUSIONS A stepwise approach to VSD enables a design to optimally meet the values of all users involved, while aligning the design process with the practical limitations of healthcare institutions. We discuss some benefits and challenges related to VSD and the potential for transfer of this approach to other digital health solutions.
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Affiliation(s)
- Merlijn Smits
- Department of Surgery, Radboud University Medical Center, Nijmegen, Gelderland, The Netherlands.
| | | | - Geke D S Ludden
- Faculty of Engineering Technology, University of Twente, Enschede, Overijssel, The Netherlands
| | - Harry van Goor
- Department of Surgery, Radboud University Medical Center, Nijmegen, Gelderland, The Netherlands
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Sujan M, Thimbleby H, Habli I, Cleve A, Maaløe L, Rees N. Assuring safe artificial intelligence in critical ambulance service response: study protocol. Br Paramed J 2022; 7:36-42. [DOI: 10.29045/14784726.2022.06.7.1.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Introduction: Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial
intelligence (AI) system has been developed to support call centre operators in the detection of OHCA. The study aims to (1) explore ambulance service stakeholder perceptions on the safety of OHCA AI decision support in call centres, and (2) develop a clinical safety case for the OHCA AI decision-support
system.Methods and analysis: The study will be undertaken within the Welsh Ambulance Service. The study is part research and part service evaluation. The research utilises a qualitative study design based on thematic analysis of interview data. The service evaluation consists of
the development of a clinical safety case based on document analysis, analysis of the AI model and its development process and informal interviews with the technology developer.Conclusions: AI presents many opportunities for ambulance services, but safety assurance requirements
need to be understood. The ASSIST project will continue to explore and build the body of knowledge in this area.
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Affiliation(s)
- Mark Sujan
- Human Factors Everywhere Ltd. ORCID iD:, URL: https://orcid.org/0000-0001-6895-946X
| | | | | | | | | | - Nigel Rees
- Welsh Ambulance Service NHS Trust ORCID iD:, URL: https://orcid.org/0000-0001-8799-5335
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31
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Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci 2022; 99:217-223. [DOI: 10.1016/j.jocn.2022.03.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/17/2022] [Accepted: 03/08/2022] [Indexed: 12/22/2022]
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AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.
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Singh D, Nagaraj S, Mashouri P, Drysdale E, Fischer J, Goldenberg A, Brudno M. Assessment of Machine Learning-Based Medical Directives to Expedite Care in Pediatric Emergency Medicine. JAMA Netw Open 2022; 5:e222599. [PMID: 35294539 PMCID: PMC8928004 DOI: 10.1001/jamanetworkopen.2022.2599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
IMPORTANCE Increased wait times and long lengths of stay in emergency departments (EDs) are associated with poor patient outcomes. Systems to improve ED efficiency would be useful. Specifically, minimizing the time to diagnosis by developing novel workflows that expedite test ordering can help accelerate clinical decision-making. OBJECTIVE To explore the use of machine learning-based medical directives (MLMDs) to automate diagnostic testing at triage for patients with common pediatric ED diagnoses. DESIGN, SETTING, AND PARTICIPANTS Machine learning models trained on retrospective electronic health record data were evaluated in a decision analytical model study conducted at the ED of the Hospital for Sick Children Toronto, Canada. Data were collected on all patients aged 0 to 18 years presenting to the ED from July 1, 2018, to June 30, 2019 (77 219 total patient visits). EXPOSURE Machine learning models were trained to predict the need for urinary dipstick testing, electrocardiogram, abdominal ultrasonography, testicular ultrasonography, bilirubin level testing, and forearm radiographs. MAIN OUTCOMES AND MEASURES Models were evaluated using area under the receiver operator curve, true-positive rate, false-positive rate, and positive predictive values. Model decision thresholds were determined to limit the total number of false-positive results and achieve high positive predictive values. The time difference between patient triage completion and test ordering was assessed for each use of MLMD. Error rates were analyzed to assess model bias. In addition, model explainability was determined using Shapley Additive Explanations values. RESULTS There was a total of 42 238 boys (54.7%) included in model development; mean (SD) age of the children was 5.4 (4.8) years. Models obtained high area under the receiver operator curve (0.89-0.99) and positive predictive values (0.77-0.94) across each of the use cases. The proposed implementation of MLMDs would streamline care for 22.3% of all patient visits and make test results available earlier by 165 minutes (weighted mean) per affected patient. Model explainability for each MLMD demonstrated clinically relevant features having the most influence on model predictions. Models also performed with minimal to no sex bias. CONCLUSIONS AND RELEVANCE The findings of this study suggest the potential for clinical automation using MLMDs. When integrated into clinical workflows, MLMDs may have the potential to autonomously order common ED tests early in a patient's visit with explainability provided to patients and clinicians.
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Affiliation(s)
- Devin Singh
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sujay Nagaraj
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Pouria Mashouri
- DATA Team, Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Erik Drysdale
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jason Fischer
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario Canada
- Canadian Institute for Advanced Research, Toronto, Ontario Canada
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- DATA Team, Techna Institute, University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario Canada
- Canadian Institute for Advanced Research, Toronto, Ontario Canada
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Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2021.102855] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Hendrix N, Veenstra DL, Cheng M, Anderson NC, Verguet S. Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:331-339. [PMID: 35227443 DOI: 10.1016/j.jval.2021.08.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods. METHODS We used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use. RESULTS We found that the measurement of core elements of value-health outcomes and cost-are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians' productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians' workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians' work can improve productivity but may reduce skills. CONCLUSIONS The potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.
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Affiliation(s)
- Nathaniel Hendrix
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - David L Veenstra
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle, WA, USA
| | - Mindy Cheng
- Global Access and Health Economics, Roche Molecular Systems, Inc, Pleasanton, CA, USA
| | | | - Stéphane Verguet
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Cifra CL, Custer JW, Fackler JC. A Research Agenda for Diagnostic Excellence in Critical Care Medicine. Crit Care Clin 2022; 38:141-157. [PMID: 34794628 PMCID: PMC8963385 DOI: 10.1016/j.ccc.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Diagnosing critically ill patients in the intensive care unit is difficult. As a result, diagnostic errors in the intensive care unit are common and have been shown to cause harm. Research to improve diagnosis in critical care medicine has accelerated in past years. However, much work remains to fully elucidate the diagnostic process in critical care. To achieve diagnostic excellence, interdisciplinary research is needed, adopting a balanced strategy of continued biomedical discovery while addressing the complex care delivery systems underpinning the diagnosis of critical illness.
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Zheng L, Liu W, Chen H. Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2022. [DOI: 10.1166/jmihi.2022.3782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence
speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution
accuracy and slow convergence speed. In the classification stage, we try to apply machine learning technology to classify the labeled feature vectors, evaluate and analyze the performance of these two machine learning algorithms in intelligent medical diagnosis auxiliary applications, and
use today’s popular deep learning classification methods (i.e., intelligently optimized text classification model) and machine learning classification method to compare the classification effect, evaluate and analyze the applicability of the classification model in the auxiliary application
of intelligent medical diagnosis. The experimental results show that the accuracy rate of applying the machine learning method to the judgment of the type of disease reaches more than 90%, which is fully in line with the disease judgment of the patient.
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Affiliation(s)
- Lifang Zheng
- Zhejiang Shaoxing Shengzhou People’s Hospital, Shaoxing Zhejiang, 312400, China
| | - Weixia Liu
- Zhejiang Shaoxing Shengzhou People’s Hospital, Shaoxing Zhejiang, 312400, China
| | - Hangying Chen
- Zhejiang Shaoxing Shengzhou People’s Hospital, Shaoxing Zhejiang, 312400, China
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38
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Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 77:29-52. [PMID: 34980946 PMCID: PMC8459787 DOI: 10.1016/j.inffus.2021.07.016] [Citation(s) in RCA: 139] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/25/2021] [Accepted: 07/25/2021] [Indexed: 05/04/2023]
Abstract
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.
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Affiliation(s)
- Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton Hospital, London, UK
- Imperial Institute of Advanced Technology, Hangzhou, China
| | - Qinghao Ye
- Hangzhou Ocean’s Smart Boya Co., Ltd, China
- University of California, San Diego, La Jolla, CA, USA
| | - Jun Xia
- Radiology Department, Shenzhen Second People’s Hospital, Shenzhen, China
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Medical Informatics, the Internet, and Telemedicine. Fam Med 2022. [DOI: 10.1007/978-3-030-54441-6_51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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40
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Kalyan KS, Rajasekharan A, Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. J Biomed Inform 2021; 126:103982. [PMID: 34974190 DOI: 10.1016/j.jbi.2021.103982] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 12/12/2021] [Accepted: 12/20/2021] [Indexed: 01/04/2023]
Abstract
Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power of transformers, transfer learning, and self-supervised learning (SSL). Following the success of these models in the general domain, the biomedical research community has developed various in-domain PLMs starting from BioBERT to the latest BioELECTRA and BioALBERT models. We strongly believe there is a need for a survey paper that can provide a comprehensive survey of various transformer-based biomedical pretrained language models (BPLMs). In this survey, we start with a brief overview of foundational concepts like self-supervised learning, embedding layer and transformer encoder layers. We discuss core concepts of transformer-based PLMs like pretraining methods, pretraining tasks, fine-tuning methods, and various embedding types specific to biomedical domain. We introduce a taxonomy for transformer-based BPLMs and then discuss all the models. We discuss various challenges and present possible solutions. We conclude by highlighting some of the open issues which will drive the research community to further improve transformer-based BPLMs. The list of all the publicly available transformer-based BPLMs along with their links is provided at https://mr-nlp.github.io/posts/2021/05/transformer-based-biomedical-pretrained-language-models-list/.
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Akbar S, Lyell D, Magrabi F. Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes. J Am Med Inform Assoc 2021; 28:2502-2513. [PMID: 34498063 PMCID: PMC8510331 DOI: 10.1093/jamia/ocab123] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/24/2021] [Accepted: 06/03/2021] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE The study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes. MATERIALS AND METHODS We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed. RESULTS Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed. CONCLUSIONS Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses' decision making, care delivery, and patient outcomes.
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Affiliation(s)
- Saba Akbar
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - David Lyell
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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Birkhoff DC, van Dalen ASH, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov 2021; 28:611-619. [PMID: 33625307 PMCID: PMC8450995 DOI: 10.1177/1553350621996961] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. Methods. PubMed, Embase, Cochrane Library, and IEEE Xplore were searched using inclusion criteria for relevant articles up to August 25th, 2020. No study types were excluded beforehand. Articles describing current AI applications for surgical purposes inside the OR were reviewed. Results. Nine studies were included. An overview of the researched and described applications of AI in the OR is provided, including procedure duration prediction, gesture recognition, intraoperative cancer detection, intraoperative video analysis, workflow recognition, an endoscopic guidance system, knot-tying, and automatic registration and tracking of the bone in orthopedic surgery. These technologies are compared to their, often non-AI, baseline alternatives. Conclusions. Currently described applications of AI in the OR are limited to date. They may, however, have a promising future in improving surgical precision, reduce manpower, support intraoperative decision-making, and increase surgical safety. Nonetheless, the application and implementation of AI inside the OR still has several challenges to overcome. Clear regulatory, organizational, and clinical conditions are imperative for AI to redeem its promise. Future research on use of AI in the OR should therefore focus on clinical validation of AI applications, the legal and ethical considerations, and on evaluation of implementation trajectory.
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Affiliation(s)
- David C. Birkhoff
- Department of Surgery, Amsterdam UMC, University of Amsterdam, The Netherlands
| | | | - Marlies P. Schijven
- Department of Surgery, Amsterdam Gastroenterology and Metabolism, University of Amsterdam, The Netherlands
- institution-id-type="Ringgold" />Li Ka Shing Knowledge Institute, institution-id-type="Ringgold" />St Michaels Hospital, Toronto, Canada
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Yousefi Nooraie R, Lyons PG, Baumann AA, Saboury B. Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science? PET Clin 2021; 16:643-653. [PMID: 34537134 DOI: 10.1016/j.cpet.2021.07.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Artificial intelligence (AI) has been rapidly adopted in various health care domains. Molecular imaging, accordingly, has demonstrated growing academic and commercial interest in AI. Unprepared and inequitable implementation and scale-up of AI in health care may pose challenges. Implementation of AI, as a complex intervention, may face various barriers, at individual, interindividual, organizational, health system, and community levels. To address these barriers, recommendations have been developed to consider health equity as a critical lens to sensitize implementation, engage stakeholders in implementation and evaluation, recognize and incorporate the iterative nature of implementation, and integrate equity and implementation in early-stage AI research.
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Affiliation(s)
- Reza Yousefi Nooraie
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, 265 Crittenden Blvd, Rochester, NY 14642, USA.
| | - Patrick G Lyons
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine in St Louis, 660 South Euclid Avenue, MSC 8052-43-14, St. Louis, MO 63110-1010, USA; Healthcare Innovation Lab, BJC HealthCare, St Louis, MO, USA
| | - Ana A Baumann
- Brown School of Social Work, Washington University in St. Louis, 600 S. Taylor Ave, MSC:8100-0094-02, St. Louis, MO 63110, USA
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Baltimore, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
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Abbasgholizadeh Rahimi S, Légaré F, Sharma G, Archambault P, Zomahoun HTV, Chandavong S, Rheault N, T Wong S, Langlois L, Couturier Y, Salmeron JL, Gagnon MP, Légaré J. Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal. J Med Internet Res 2021; 23:e29839. [PMID: 34477556 PMCID: PMC8449300 DOI: 10.2196/29839] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. OBJECTIVE We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. METHODS We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. RESULTS We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). CONCLUSIONS We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.
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Affiliation(s)
- Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.,Mila-Quebec AI Institute, Montreal, QC, Canada
| | - France Légaré
- Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, QC, Canada.,VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada
| | - Gauri Sharma
- Faculty of Engineering, Dayalbagh Educational Institute, Agra, India
| | - Patrick Archambault
- Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, QC, Canada.,VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada
| | - Herve Tchala Vignon Zomahoun
- VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada.,Quebec SPOR-Support Unit, Quebec City, QC, Canada
| | - Sam Chandavong
- Faculty of Science and Engineering, Université Laval, Quebec City, QC, Canada
| | - Nathalie Rheault
- VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada.,Quebec SPOR-Support Unit, Quebec City, QC, Canada
| | - Sabrina T Wong
- School of Nursing, University of British Columbia, Vancouver, BC, Canada.,Center for Health Services and Policy Research, University of British Columbia, Vancouver, BC, Canada
| | - Lyse Langlois
- Department of Industrial Relations, Université Laval, Quebec City, QC, Canada.,OBVIA - Quebec International Observatory on the social impacts of AI and digital technology, Quebec City, QC, Canada
| | - Yves Couturier
- School of Social Work, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Jose L Salmeron
- Department of Data Science, University Pablo de Olavide, Seville, Spain
| | | | - Jean Légaré
- Arthritis Alliance of Canada, Montreal, QC, Canada
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Byerly S, Maurer LR, Mantero A, Naar L, An G, Kaafarani HMA. Machine Learning and Artificial Intelligence for Surgical Decision Making. Surg Infect (Larchmt) 2021; 22:626-634. [PMID: 34270361 DOI: 10.1089/sur.2021.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background: The use of machine learning (ML) and artificial intelligence (AI) in medical research continues to grow as the amount and availability of clinical data expands. These techniques allow complex interpretation of data and capture non-linear relations not immediately apparent by classic statistical techniques. Methods: This review of the ML/AI literature provides a brief overview for practicing surgeons and clinicians of the current and future roles these methods will have within surgical infection research. Results: A conceptual overview of the techniques is provided along with concrete examples in the surgical infections literature. Further examples of ML/AI techniques in clinical decision support as well as therapy discovery with model-based deep reinforcement learning are illustrated. Conclusions: Artificial intelligence and ML are important and increasingly utilized techniques within the expanding body of surgical infection research. This article provides a minimal baseline literacy in ML/AI to be able to view such projects in an appropriately critical fashion.
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Affiliation(s)
- Saskya Byerly
- Department of Surgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Lydia R Maurer
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alejandro Mantero
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miami, Florida, USA
| | - Leon Naar
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gary An
- Department of Surgery, University of Vermont, Burlington, Vermont, USA
| | - Haytham M A Kaafarani
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Grzenda A, Kraguljac NV, McDonald WM, Nemeroff C, Torous J, Alpert JE, Rodriguez CI, Widge AS. Evaluating the Machine Learning Literature: A Primer and User's Guide for Psychiatrists. Am J Psychiatry 2021; 178:715-729. [PMID: 34080891 DOI: 10.1176/appi.ajp.2020.20030250] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Nina V Kraguljac
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - William M McDonald
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Charles Nemeroff
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - John Torous
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Jonathan E Alpert
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Carolyn I Rodriguez
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Alik S Widge
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
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Antes AL, Burrous S, Sisk BA, Schuelke MJ, Keune JD, DuBois JM. Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey. BMC Med Inform Decis Mak 2021; 21:221. [PMID: 34284756 PMCID: PMC8293482 DOI: 10.1186/s12911-021-01586-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 07/02/2021] [Indexed: 01/14/2023] Open
Abstract
Background Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions. Methods We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently. Results Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions. Conclusions Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01586-8.
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Affiliation(s)
- Alison L Antes
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
| | - Sara Burrous
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Bryan A Sisk
- Department of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Matthew J Schuelke
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Jason D Keune
- Departments of Surgery and Health Care Ethics, Bander Center for Medical Business Ethics, Saint Louis University, St. Louis, MO, USA
| | - James M DuBois
- Bioethics Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Mirbabaie M, Hofeditz L, Frick NRJ, Stieglitz S. Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research. AI & SOCIETY 2021; 37:1361-1382. [PMID: 34219989 PMCID: PMC8238382 DOI: 10.1007/s00146-021-01239-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/25/2021] [Indexed: 12/23/2022]
Abstract
The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns. Supplementary Information The online version contains supplementary material available at 10.1007/s00146-021-01239-4.
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Affiliation(s)
- Milad Mirbabaie
- Faculty of Business Administration and Economics, Paderborn University, Paderborn, Germany
| | - Lennart Hofeditz
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
| | - Nicholas R. J. Frick
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
| | - Stefan Stieglitz
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
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O'Donoghue SI. Grand Challenges in Bioinformatics Data Visualization. FRONTIERS IN BIOINFORMATICS 2021; 1:669186. [PMID: 36303723 PMCID: PMC9581027 DOI: 10.3389/fbinf.2021.669186] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/30/2021] [Indexed: 01/17/2023] Open
Affiliation(s)
- Seán I. O'Donoghue
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
- CSIRO Data61, Eveleigh, NSW, Australia
- *Correspondence: Seán I. O'Donoghue,
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A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission. NPJ Digit Med 2021; 4:98. [PMID: 34127786 PMCID: PMC8203794 DOI: 10.1038/s41746-021-00468-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 05/21/2021] [Indexed: 01/23/2023] Open
Abstract
The interest in applying machine learning in healthcare has grown rapidly in recent years. Most predictive algorithms requiring pathway implementations are evaluated using metrics focused on predictive performance, such as the c statistic. However, these metrics are of limited clinical value, for two reasons: (1) they do not account for the algorithm's role within a provider workflow; and (2) they do not quantify the algorithm's value in terms of patient outcomes and cost savings. We propose a model for simulating the selection of patients over time by a clinician using a machine learning algorithm, and quantifying the expected patient outcomes and cost savings. Using data on unplanned emergency department surgical readmissions, we show that factors such as the provider's schedule and postoperative prediction timing can have major effects on the pathway cohort size and potential cost reductions from preventing hospital readmissions.
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