1
|
Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [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: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
Collapse
Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| |
Collapse
|
2
|
Jin K, Li Y, Wu H, Tham YC, Koh V, Zhao Y, Kawasaki R, Grzybowski A, Ye J. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2024; 4:120-127. [PMID: 38846624 PMCID: PMC11154117 DOI: 10.1016/j.aopr.2024.03.003] [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: 01/14/2024] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 06/09/2024]
Abstract
Background The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.
Collapse
Affiliation(s)
- Kai Jin
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yingyu Li
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Hongkang Wu
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Victor Koh
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Ningbo Eye Hospital, Ningbo, China
- Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Ryo Kawasaki
- Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| |
Collapse
|
3
|
Wu H, Jin K, Yip CC, Koh V, Ye J. A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality. Surv Ophthalmol 2024; 69:499-507. [PMID: 38492584 DOI: 10.1016/j.survophthal.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024]
Abstract
Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.
Collapse
Affiliation(s)
- Hongkang Wu
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chee Chew Yip
- Department of Ophthalmology & Visual Sciences, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, National University of Singapore, Singapore
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| |
Collapse
|
4
|
Slominski RM, Kim TK, Janjetovic Z, Brożyna AA, Podgorska E, Dixon KM, Mason RS, Tuckey RC, Sharma R, Crossman DK, Elmets C, Raman C, Jetten AM, Indra AK, Slominski AT. Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling. Cancers (Basel) 2024; 16:2262. [PMID: 38927967 DOI: 10.3390/cancers16122262] [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] [Received: 05/04/2024] [Revised: 06/09/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.
Collapse
Affiliation(s)
- Radomir M Slominski
- Department of Rheumatology and Clinical Immunology, Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Tae-Kang Kim
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Zorica Janjetovic
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Anna A Brożyna
- Department of Human Biology, Institute of Biology, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, 87-100 Torun, Poland
| | - Ewa Podgorska
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Katie M Dixon
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia
| | - Rebecca S Mason
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia
| | - Robert C Tuckey
- School of Molecular Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - Rahul Sharma
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - David K Crossman
- Department of Genetics and Bioinformatics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Craig Elmets
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Chander Raman
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Anton M Jetten
- Cell Biology Section, NIEHS-National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Arup K Indra
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR 97331, USA
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andrzej T Slominski
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Pathology and Laboratory Medicine Service, Veteran Administration Medical Center, Birmingham, AL 35233, USA
| |
Collapse
|
5
|
Yao J, Lim J, Lim GYS, Ong JCL, Ke Y, Tan TF, Tan TE, Vujosevic S, Ting DSW. Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema. EYE AND VISION (LONDON, ENGLAND) 2024; 11:23. [PMID: 38880890 PMCID: PMC11181581 DOI: 10.1186/s40662-024-00389-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/09/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. MAIN TEXT This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. CONCLUSION AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
Collapse
Affiliation(s)
- Jie Yao
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Joshua Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Gilbert Yong San Lim
- Duke-NUS Medical School, Singapore, Singapore
- SingHealth AI Health Program, Singapore, Singapore
| | - Jasmine Chiat Ling Ong
- Duke-NUS Medical School, Singapore, Singapore
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore
| | - Yuhe Ke
- Department of Anesthesiology and Perioperative Science, Singapore General Hospital, Singapore, Singapore
| | - Ting Fang Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Eye Clinic, IRCCS MultiMedica, Milan, Italy
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- SingHealth AI Health Program, Singapore, Singapore.
| |
Collapse
|
6
|
Sezgin E, McKay I. Behavioral health and generative AI: a perspective on future of therapies and patient care. NPJ MENTAL HEALTH RESEARCH 2024; 3:25. [PMID: 38849499 PMCID: PMC11161641 DOI: 10.1038/s44184-024-00067-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/06/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Emre Sezgin
- The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
- The Ohio State University College of Medicine, Columbus, OH, USA.
| | - Ian McKay
- The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Psychiatry and Behavioral Health, Nationwide Children's Hospital, Columbus, OH, USA
| |
Collapse
|
7
|
Karabeg M, Petrovski G, Hertzberg SN, Erke MG, Fosmark DS, Russell G, Moe MC, Volke V, Raudonis V, Verkauskiene R, Sokolovska J, Haugen IBK, Petrovski BE. A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway. Int J Retina Vitreous 2024; 10:40. [PMID: 38783384 PMCID: PMC11112837 DOI: 10.1186/s40942-024-00547-3] [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] [Received: 01/14/2024] [Accepted: 03/17/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed. PURPOSE To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images. METHODS On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods. RESULTS 33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI. CONCLUSION Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.
Collapse
Affiliation(s)
- Mia Karabeg
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Goran Petrovski
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, University of Split School of Medicine and University Hospital Centre, 21000, Split, Croatia
- UKLONetwork, University St. Kliment Ohridski-Bitola, 7000, Bitola, Macedonia
| | - Silvia Nw Hertzberg
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Maja Gran Erke
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Dag Sigurd Fosmark
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Greg Russell
- Clinical Development, Eyenuk Inc, Woodland Hills, CA, USA
| | - Morten C Moe
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Vallo Volke
- Faculty of Medicine, Tartu University, 50411, Tartu, Estonia
| | - Vidas Raudonis
- Automation Department, Kaunas University of Technology, 51368, Kaunas, Lithuania
| | - Rasa Verkauskiene
- Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania
| | | | | | - Beata Eva Petrovski
- Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway.
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway.
- Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania.
| |
Collapse
|
8
|
Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, Chalkidou A, Petrou S, Petykó Z, Srivastava D, Chandra G, Delaye J, Denniston A, Gomes M, Knies S, Nousios P, Siirtola P, Wang J, Dawoud D. Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI). VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024:S1098-3015(24)02366-0. [PMID: 38795956 DOI: 10.1016/j.jval.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/22/2024] [Accepted: 05/04/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVES Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.
Collapse
Affiliation(s)
- Jamie Elvidge
- National Institute for Health and Care Excellence (NICE), England, UK.
| | - Claire Hawksworth
- National Institute for Health and Care Excellence (NICE), England, UK
| | - Tuba Saygın Avşar
- National Institute for Health and Care Excellence (NICE), England, UK
| | - Antal Zemplenyi
- Center for Health Technology Assessment and Pharmacoeconomic Research, University of Pécs, Pécs, Hungary; University of Colorado Anschutz Medical Campus, Denver, CO, USA; Syreon Research Institute, Budapest, Hungary
| | | | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, England, UK
| | | | - Divya Srivastava
- Department of Health Policy, London School of Economics and Political Science, London, England, UK
| | - Gunjan Chandra
- Biomimetics and Intelligent Systems Group, University of Oulu, Oulu, Finland
| | | | - Alastair Denniston
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, England, UK
| | - Manuel Gomes
- Department of Primary Care and Population Health, University College London, England, UK
| | - Saskia Knies
- National Healthcare Institute (ZIN), Diemen, The Netherlands
| | - Petros Nousios
- Dental and Pharmaceutical Benefits Agency (TLV), Stockholm, Sweden
| | - Pekka Siirtola
- Biomimetics and Intelligent Systems Group, University of Oulu, Oulu, Finland
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, The Netherlands
| | - Dalia Dawoud
- National Institute for Health and Care Excellence (NICE), England, UK
| |
Collapse
|
9
|
Farah L, Borget I, Martelli N, Vallee A. Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review. J Med Internet Res 2024; 26:e51514. [PMID: 38739911 PMCID: PMC11130781 DOI: 10.2196/51514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.
Collapse
Affiliation(s)
- Line Farah
- Innovation Center for Medical Devices Department, Foch Hospital, Suresnes, France
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
| | - Isabelle Borget
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre le Cancer, University Paris-Saclay, Villejuif, France
| | - Nicolas Martelli
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France
- Pharmacy Department, Georges Pompidou European Hospital, Paris, France
| | - Alexandre Vallee
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
| |
Collapse
|
10
|
Haykal D, Garibyan L, Flament F, Cartier H. Hybrid cosmetic dermatology: AI generated horizon. Skin Res Technol 2024; 30:e13721. [PMID: 38696225 PMCID: PMC11064925 DOI: 10.1111/srt.13721] [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: 12/10/2023] [Accepted: 04/15/2024] [Indexed: 05/04/2024]
Affiliation(s)
| | - Lilit Garibyan
- Wellman Center for PhotomedicineMassachusetts General HospitalBostonMassachusettsUSA
- Department of DermatologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | | |
Collapse
|
11
|
Wu X, Wu Y, Tu Z, Cao Z, Xu M, Xiang Y, Lin D, Jin L, Zhao L, Zhang Y, Liu Y, Yan P, Hu W, Liu J, Liu L, Wang X, Wang R, Chen J, Xiao W, Shang Y, Xie P, Wang D, Zhang X, Dongye M, Wang C, Ting DSW, Liu Y, Pan R, Lin H. Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China. Nat Commun 2024; 15:3650. [PMID: 38688925 PMCID: PMC11061155 DOI: 10.1038/s41467-024-47211-w] [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: 07/18/2023] [Accepted: 03/25/2024] [Indexed: 05/02/2024] Open
Abstract
Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.
Collapse
Affiliation(s)
- Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Zhenjun Tu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zizheng Cao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Miaohong Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yifan Xiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Ling Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yingzhe Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yu Liu
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Pisong Yan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Weiling Hu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Jiali Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Lixue Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Ruixin Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Jieying Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wei Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yuanjun Shang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Peichen Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Dongni Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Xulin Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Meimei Dongye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Chenxinqi Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
| | - Rong Pan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan, China.
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
| |
Collapse
|
12
|
Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
Collapse
Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| |
Collapse
|
13
|
Wong CYT, O'Byrne C, Taribagil P, Liu T, Antaki F, Keane PA. Comparing code-free and bespoke deep learning approaches in ophthalmology. Graefes Arch Clin Exp Ophthalmol 2024:10.1007/s00417-024-06432-x. [PMID: 38446200 DOI: 10.1007/s00417-024-06432-x] [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: 11/20/2023] [Revised: 02/13/2024] [Accepted: 02/27/2024] [Indexed: 03/07/2024] Open
Abstract
AIM Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writing code. In this review, we comprehensively review the advantages that CFDL offers over bespoke expert-designed deep learning (DL). As exemplars, we use the following tasks: (1) diabetic retinopathy screening, (2) retinal multi-disease classification, (3) surgical video classification, (4) oculomics and (5) resource management. METHODS We performed a search for studies reporting CFDL applications in ophthalmology in MEDLINE (through PubMed) from inception to June 25, 2023, using the keywords 'autoML' AND 'ophthalmology'. After identifying 5 CFDL studies looking at our target tasks, we performed a subsequent search to find corresponding bespoke DL studies focused on the same tasks. Only English-written articles with full text available were included. Reviews, editorials, protocols and case reports or case series were excluded. We identified ten relevant studies for this review. RESULTS Overall, studies were optimistic towards CFDL's advantages over bespoke DL in the five ophthalmological tasks. However, much of such discussions were identified to be mono-dimensional and had wide applicability gaps. High-quality assessment of better CFDL applicability over bespoke DL warrants a context-specific, weighted assessment of clinician intent, patient acceptance and cost-effectiveness. We conclude that CFDL and bespoke DL are unique in their own assets and are irreplaceable with each other. Their benefits are differentially valued on a case-to-case basis. Future studies are warranted to perform a multidimensional analysis of both techniques and to improve limitations of suboptimal dataset quality, poor applicability implications and non-regulated study designs. CONCLUSION For clinicians without DL expertise and easy access to AI experts, CFDL allows the prototyping of novel clinical AI systems. CFDL models concert with bespoke models, depending on the task at hand. A multidimensional, weighted evaluation of the factors involved in the implementation of those models for a designated task is warranted.
Collapse
Affiliation(s)
- Carolyn Yu Tung Wong
- Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ciara O'Byrne
- Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Priyal Taribagil
- Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Timing Liu
- Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Fares Antaki
- Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- The CHUM School of Artificial Intelligence in Healthcare, Montreal, QC, Canada
| | - Pearse Andrew Keane
- Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK.
- Moorfields Eye Hospital NHS Foundation Trust, London, UK.
- NIHR Moorfields Biomedical Research Centre, London, UK.
| |
Collapse
|
14
|
Tan CH, Goh WWB, So JBY, Sung JJY. Clinical use cases in artificial intelligence: current trends and future opportunities. Singapore Med J 2024; 65:183-185. [PMID: 38527304 PMCID: PMC11060646 DOI: 10.4103/singaporemedj.smj-2023-193] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/19/2024] [Indexed: 03/27/2024]
Affiliation(s)
- Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore
| | - Jimmy Bok Yan So
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
- Division of Surgical Oncology, National University Cancer Institute, Singapore
| | - Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, Singapore
| |
Collapse
|
15
|
Silva PS, Zhang D, Jacoba CMP, Fickweiler W, Lewis D, Leitmeyer J, Curran K, Salongcay RP, Doan D, Ashraf M, Cavallerano JD, Sun JK, Peto T, Aiello LP. Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images. JAMA Ophthalmol 2024; 142:171-177. [PMID: 38329765 PMCID: PMC10853872 DOI: 10.1001/jamaophthalmol.2023.6318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/24/2023] [Indexed: 02/09/2024]
Abstract
Importance Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. Objective To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images. Design, Setting and Participants Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022. Exposure Automated ML models were generated from baseline on-axis 200° UWF retinal images. Baseline retinal images were labeled for progression based on centralized reading center evaluation of baseline and follow-up images according to the clinical Early Treatment Diabetic Retinopathy Study severity scale. Images for model development were split 8-1-1 for training, optimization, and testing to detect 1 or more steps of DR progression. Validation was performed using a 328-image set from the same patient population not used in model development. Main Outcomes and Measures Area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy. Results A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moderate (799 [67.8%]) NPDR were included. DR progression was present in half of the training set (590 of 1179 [50.0%]). The model's AUPRC was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. On the validation set for eyes with mild NPDR, sensitivity was 0.72 (95% CI, 0.57-0.83), specificity was 0.63 (95% CI, 0.57-0.69), prevalence was 0.15 (95% CI, 0.12-0.20), and accuracy was 64.3%; for eyes with moderate NPDR, sensitivity was 0.80 (95% CI, 0.70-0.87), specificity was 0.72 (95% CI, 0.66-0.76), prevalence was 0.22 (95% CI, 0.19-0.27), and accuracy was 73.8%. In the validation set, 6 of 9 eyes (75%) with mild NPDR and 35 of 41 eyes (85%) with moderate NPDR progressed 2 steps or more were identified. All 4 eyes with mild NPDR that progressed within 6 months and 1 year were identified, and 8 of 9 (89%) and 17 of 20 (85%) with moderate NPDR that progressed within 6 months and 1 year, respectively, were identified. Conclusions and Relevance This study demonstrates the accuracy and feasibility of automated ML models for identifying DR progression developed using UWF images, especially for prediction of 2-step or greater DR progression within 1 year. Potentially, the use of ML algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes.
Collapse
Affiliation(s)
- Paolo S. Silva
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Dean Zhang
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Cris Martin P. Jacoba
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Ward Fickweiler
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Drew Lewis
- Estenda Solutions, Conshohocken, Pennsylvania
| | | | - Katie Curran
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | | | - Duy Doan
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Mohamed Ashraf
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Jerry D. Cavallerano
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Jennifer K. Sun
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Tunde Peto
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Lloyd Paul Aiello
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
- Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
16
|
Ueno Y, Oda M, Yamaguchi T, Fukuoka H, Nejima R, Kitaguchi Y, Miyake M, Akiyama M, Miyata K, Kashiwagi K, Maeda N, Shimazaki J, Noma H, Mori K, Oshika T. Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. Br J Ophthalmol 2024:bjo-2023-324488. [PMID: 38242700 DOI: 10.1136/bjo-2023-324488] [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: 08/25/2023] [Accepted: 12/17/2023] [Indexed: 01/21/2024]
Abstract
AIM To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images. METHODS This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases. RESULTS The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for 'urgent', 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for 'semi-urgent', 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for 'routine' and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for 'observation', respectively. CONCLUSIONS The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.
Collapse
Affiliation(s)
- Yuta Ueno
- Department of Ophthalmology, University of Tsukuba, Tsukuba, Japan
| | - Masahiro Oda
- Information Technology Center, Nagoya University, Nagoya, Japan
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Takefumi Yamaguchi
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Ichikawa, Japan
| | - Hideki Fukuoka
- Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | | | - Yoshiyuki Kitaguchi
- Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masahiro Miyake
- Department of Ophthalmology and Vusual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Kyushu University, Fukuoka, Japan
| | | | - Kenji Kashiwagi
- Department of Ophthalmology, University of Yamanashi, Kofu, Japan
| | - Naoyuki Maeda
- Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Jun Shimazaki
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Ichikawa, Japan
| | - Hisashi Noma
- Department of Data Science, Institute of Statistical Mathematics, Tokyo, Japan
| | - Kensaku Mori
- Information Technology Center, Nagoya University, Nagoya, Japan
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
- National Institute of Informatics, Tokyo, Japan
| | - Tetsuro Oshika
- Department of Ophthalmology, University of Tsukuba, Tsukuba, Japan
| |
Collapse
|
17
|
Ong JCL, Seng BJJ, Law JZF, Low LL, Kwa ALH, Giacomini KM, Ting DSW. Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions. Cell Rep Med 2024; 5:101356. [PMID: 38232690 PMCID: PMC10829781 DOI: 10.1016/j.xcrm.2023.101356] [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: 05/03/2023] [Revised: 10/12/2023] [Accepted: 12/10/2023] [Indexed: 01/19/2024]
Abstract
This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global problem exacerbated by the COVID-19 pandemic. We provide a broad discussion on current developments in digital health and artificial intelligence (AI), including large language models (LLMs), as transformative tools in addressing SDOH factors, offering new capabilities for disease surveillance and patient care. Simultaneously, we bring attention to challenges, such as data standardization, infrastructure limitations, digital literacy, and algorithmic bias, that could hinder equitable access to AI benefits. For LLMs, we highlight potential unique challenges and risks including environmental impact, unfair labor practices, inadvertent disinformation or "hallucinations," proliferation of bias, and infringement of copyrights. We propose the need for a multitiered approach to digital inclusion as an SDOH and the development of ethical and responsible AI practice frameworks globally and provide suggestions on bridging the gap from development to implementation of equitable AI technologies.
Collapse
Affiliation(s)
- Jasmine Chiat Ling Ong
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore; SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore, Singapore
| | - Benjamin Jun Jie Seng
- MOHH Holdings (Singapore) Pte., Ltd., Singapore, Singapore; SingHealth Duke-NUS Family Medicine Academic Clinical Programme, Singapore, Singapore
| | | | - Lian Leng Low
- SingHealth Duke-NUS Family Medicine Academic Clinical Programme, Singapore, Singapore; Population Health and Integrated Care Office, Singapore General Hospital, Singapore, Singapore; Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore; Outram Community Hospital, SingHealth Community Hospitals, Singapore, Singapore
| | - Andrea Lay Hoon Kwa
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore; SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore, Singapore; Emerging Infectious Diseases, Duke-NUS Medical School, Singapore, Singapore
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research, Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore; Byers Eye Institute, Stanford University, Stanford, CA, USA.
| |
Collapse
|
18
|
Hu W, Joseph S, Li R, Woods E, Sun J, Shen M, Jan CL, Zhu Z, He M, Zhang L. Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis. EClinicalMedicine 2024; 67:102387. [PMID: 38314061 PMCID: PMC10837545 DOI: 10.1016/j.eclinm.2023.102387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 02/06/2024] Open
Abstract
Background We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia. Methods We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations. Funding This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Collapse
Affiliation(s)
- Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Sanil Joseph
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Rui Li
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, 710061, PR China
| | - Ekaterina Woods
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Jason Sun
- Eyetelligence Pty Ltd., Melbourne, Australia
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, 710061, PR China
| | - Catherine Lingxue Jan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Lei Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210008, China
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
| |
Collapse
|
19
|
Rajesh AE, Davidson OQ, Lee CS, Lee AY. Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness. Diabetes Care 2023; 46:1728-1739. [PMID: 37729502 PMCID: PMC10516248 DOI: 10.2337/dci23-0032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/15/2023] [Indexed: 09/22/2023]
Abstract
Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.
Collapse
Affiliation(s)
- Anand E. Rajesh
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Oliver Q. Davidson
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| |
Collapse
|
20
|
Ruamviboonsuk P, Ruamviboonsuk V, Tiwari R. Recent evidence of economic evaluation of artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:449-458. [PMID: 37459289 DOI: 10.1097/icu.0000000000000987] [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: 08/12/2023]
Abstract
PURPOSE OF REVIEW Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed. RECENT FINDINGS Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models. SUMMARY Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.
Collapse
Affiliation(s)
- Paisan Ruamviboonsuk
- Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University
| | | | | |
Collapse
|
21
|
Ahmed A, Dujaili JA, Chuah LH, Hashmi FK, Le LKD, Khanal S, Awaisu A, Chaiyakunapruk N. Cost-Effectiveness of Anti-retroviral Adherence Interventions for People Living with HIV: A Systematic Review of Decision Analytical Models. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2023; 21:731-750. [PMID: 37389788 PMCID: PMC10403422 DOI: 10.1007/s40258-023-00818-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/05/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND Although safe and effective anti-retrovirals (ARVs) are readily available, non-adherence to ARVs is highly prevalent among people living with human immunodeficiency virus/acquired immunodeficiency syndrome (PLWHA). Different adherence-improving interventions have been developed and examined through decision analytic model-based health technology assessments. This systematic review aimed to review and appraise the decision analytical economic models developed to assess ARV adherence-improvement interventions. METHODS The review protocol was registered on PROSPERO (CRD42022270039), and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Relevant studies were identified through searches in six generic and specialized bibliographic databases, i.e. PubMed, Embase, NHS Economic Evaluation Database, PsycINFO, Health Economic Evaluations Database, tufts CEA registry and EconLit, from their inception to 23 October 2022. The cost-effectiveness of adherence interventions is represented by the incremental cost-effectiveness ratio (ICER). The quality of studies was assessed using the quality of the health economics studies (QHES) instrument. Data were narratively synthesized in the form of tables and texts. Due to the heterogeneity of the data, a permutation matrix was used for quantitative data synthesis rather than a meta-analysis. RESULTS Fifteen studies, mostly conducted in North America (8/15 studies), were included in the review. The time horizon ranged from a year to a lifetime. Ten out of 15 studies used a micro-simulation, 4/15 studies employed Markov and 1/15 employed a dynamic model. The most commonly used interventions reported include technology based (5/15), nurse involved (2/15), directly observed therapy (2/15), case manager involved (1/15) and others that involved multi-component interventions (5/15). In 1/15 studies, interventions gained higher quality-adjusted life years (QALYs) with cost savings. The interventions in 14/15 studies were more effective but at a higher cost, and the overall ICER was well below the acceptable threshold mentioned in each study, indicating the interventions could potentially be implemented after careful interpretation. The studies were graded as high quality (13/15) or fair quality (2/15), with some methodological inconsistencies reported. CONCLUSION Counselling and smartphone-based interventions are cost-effective, and they have the potential to reduce the chronic adherence problem significantly. The quality of decision models can be improved by addressing inconsistencies in model selection, data inputs incorporated into models and uncertainty assessment methods.
Collapse
Affiliation(s)
- Ali Ahmed
- School of Pharmacy, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia.
| | - Juman Abdulelah Dujaili
- School of Pharmacy, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia
- Swansea University Medical School, Singleton Campus, Swansea University, Wales, UK
| | - Lay Hong Chuah
- School of Pharmacy, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia
| | - Furqan Khurshid Hashmi
- University College of Pharmacy, University of Punjab, Allama Iqbal Campus, Lahore, 54000, Pakistan
| | - Long Khanh-Dao Le
- Monash University Health Economics Group (MUHEG), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Saval Khanal
- Health Economics Consulting, University of East Anglia, Coventry, UK
| | - Ahmed Awaisu
- Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar
| | - Nathorn Chaiyakunapruk
- College of Pharmacy, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| |
Collapse
|
22
|
Rizvi A, Rizvi F, Lalakia P, Hyman L, Frasso R, Sztandera L, Das AV. Is Artificial Intelligence the Cost-Saving Lens to Diabetic Retinopathy Screening in Low- and Middle-Income Countries? Cureus 2023; 15:e45539. [PMID: 37868419 PMCID: PMC10586227 DOI: 10.7759/cureus.45539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/24/2023] Open
Abstract
Diabetes is a rapidly growing global health crisis disproportionately affecting low- and middle-income countries (LMICs). The emergence of diabetes as a global pandemic is one of the major challenges to human health, as long-term microvascular complications such as diabetic retinopathy (DR) can lead to irreversible blindness. Leveraging artificial intelligence (AI) technology may improve the diagnostic accuracy, efficiency, and accessibility of DR screenings across LMICs. However, there is a gap between the potential of AI technology and its implementation in clinical practice. The main objective of this systematic review is to summarize the currently available literature on the health economic assessments of AI implementation for DR screening in LMICs. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an extensive systematic search of PubMed/MEDLINE, Scopus, and the Web of Science on July 15, 2023. Our review included full-text English-language articles from any publication year. The Joanna Briggs Institute's (JBI) critical appraisal checklist for economic evaluations was used to rate the quality and rigor of the selected articles. The initial search generated 1,423 records and was narrowed to five full-text articles through comprehensive inclusion and exclusion criteria. Of the five articles included in our systematic review, two used a cost-effectiveness analysis, two used a cost-utility analysis, and one used both a cost-effectiveness analysis and a cost-utility analysis. Across the five articles, LMICs such as China, Thailand, and Brazil were represented in the economic evaluations and models. Overall, three out of the five articles concluded that AI-based DR screening was more cost-effective in comparison to standard-of-care screening methods. Our systematic review highlights the need for more primary health economic analyses that carefully evaluate the economic implications of adopting AI technology for DR screening in LMICs. We hope this systematic review will offer valuable guidance to healthcare providers, scientists, and legislators to support appropriate decision-making regarding the implementation of AI algorithms for DR screening in healthcare workflows.
Collapse
Affiliation(s)
- Anza Rizvi
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, USA
- College of Population Health, Thomas Jefferson University, Philadelphia, USA
| | - Fatima Rizvi
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, USA
- College of Population Health, Thomas Jefferson University, Philadelphia, USA
| | - Parth Lalakia
- College of Population Health, Thomas Jefferson University, Philadelphia, USA
- Osteopathic Medicine, Rowan-Virtua School of Osteopathic Medicine, Stratford, USA
- Office of Global Affairs, Thomas Jefferson University, Philadelphia, USA
| | - Leslie Hyman
- Geriatric Medicine and Palliative Care, Department of Family Medicine, Thomas Jefferson University, Philadelphia, USA
- The Vickie and Jack Farber Vision Research Center, Wills Eye Hospital, Philadelphia, USA
| | - Rosemary Frasso
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, USA
- College of Population Health, Thomas Jefferson University, Philadelphia, USA
- Asano-Gonnella Center for Research in Medical Education and Health Care, Thomas Jefferson University, Philadelphia, USA
| | - Les Sztandera
- Kanbar College of Design, Engineering, and Commerce, Thomas Jefferson University, Philadelphia, USA
| | | |
Collapse
|
23
|
Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
Collapse
Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
24
|
Jacoba CMP, Doan D, Salongcay RP, Aquino LAC, Silva JPY, Salva CMG, Zhang D, Alog GP, Zhang K, Locaylocay KLRB, Saunar AV, Ashraf M, Sun JK, Peto T, Aiello LP, Silva PS. Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images. Ophthalmol Retina 2023; 7:703-712. [PMID: 36924893 DOI: 10.1016/j.oret.2023.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/07/2023] [Accepted: 03/01/2023] [Indexed: 03/17/2023]
Abstract
PURPOSE To create and validate code-free automated deep learning models (AutoML) for diabetic retinopathy (DR) classification from handheld retinal images. DESIGN Prospective development and validation of AutoML models for DR image classification. PARTICIPANTS A total of 17 829 deidentified retinal images from 3566 eyes with diabetes, acquired using handheld retinal cameras in a community-based DR screening program. METHODS AutoML models were generated based on previously acquired 5-field (macula-centered, disc-centered, superior, inferior, and temporal macula) handheld retinal images. Each individual image was labeled using the International DR and diabetic macular edema (DME) Classification Scale by 4 certified graders at a centralized reading center under oversight by a senior retina specialist. Images for model development were split 8-1-1 for training, optimization, and testing to detect referable DR ([refDR], defined as moderate nonproliferative DR or worse or any level of DME). Internal validation was performed using a published image set from the same patient population (N = 450 images from 225 eyes). External validation was performed using a publicly available retinal imaging data set from the Asia Pacific Tele-Ophthalmology Society (N = 3662 images). MAIN OUTCOME MEASURES Area under the precision-recall curve (AUPRC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 scores. RESULTS Referable DR was present in 17.3%, 39.1%, and 48.0% of the training set, internal validation, and external validation sets, respectively. The model's AUPRC was 0.995 with a precision and recall of 97% using a score threshold of 0.5. Internal validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.96 (95% confidence interval [CI], 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.96 (95% CI, 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.97, and 0.96, respectively. External validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.94 (95% CI, 0.929-0.951), 0.97 (95% CI, 0.957-0.974), 0.96 (95% CI, 0.952-0.971), 0.95 (95% CI, 0.935-0.956), 0.97, and 0.96, respectively. CONCLUSIONS This study demonstrates the accuracy and feasibility of code-free AutoML models for identifying refDR developed using handheld retinal imaging in a community-based screening program. Potentially, the use of AutoML may increase access to machine learning models that may be adapted for specific programs that are guided by the clinical need to rapidly address disparities in health care delivery. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
Collapse
Affiliation(s)
- Cris Martin P Jacoba
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Duy Doan
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Recivall P Salongcay
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Centre for Public Health, Queen's University Belfast, United Kingdom; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Lizzie Anne C Aquino
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| | - Joseph Paolo Y Silva
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| | | | - Dean Zhang
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Glenn P Alog
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Kexin Zhang
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts
| | - Kaye Lani Rea B Locaylocay
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Aileen V Saunar
- Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines
| | - Mohamed Ashraf
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Jennifer K Sun
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, United Kingdom
| | - Lloyd Paul Aiello
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts
| | - Paolo S Silva
- Beetham Eye Institute, Joslin Diabetes Center, Boston, Massachusetts; Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts; Philippine Eye Research Institute, University of the Philippines, Manila, Philippines; Eyes and Vision Institute, the Medical City, Pasig City, Philippines.
| |
Collapse
|
25
|
Cleland CR, Rwiza J, Evans JR, Gordon I, MacLeod D, Burton MJ, Bascaran C. Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review. BMJ Open Diabetes Res Care 2023; 11:e003424. [PMID: 37532460 PMCID: PMC10401245 DOI: 10.1136/bmjdrc-2023-003424] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.
Collapse
Affiliation(s)
- Charles R Cleland
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania
| | - Justus Rwiza
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, United Republic of Tanzania
| | - Jennifer R Evans
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Iris Gordon
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - David MacLeod
- Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew J Burton
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Covadonga Bascaran
- International Centre for Eye Health, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| |
Collapse
|
26
|
Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, Hanisch M, Vinayahalingam S. Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med 2023; 19:23. [PMID: 37349791 PMCID: PMC10288769 DOI: 10.1186/s13005-023-00368-z] [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: 01/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.
Collapse
Affiliation(s)
- Nasim Ayad
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Stefanie van den Bosch
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Lauren Bohner
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| |
Collapse
|
27
|
Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Front Endocrinol (Lausanne) 2023; 14:1197783. [PMID: 37383397 PMCID: PMC10296189 DOI: 10.3389/fendo.2023.1197783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/23/2023] [Indexed: 06/30/2023] Open
Abstract
Aims To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness. Materials and methods A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm. Results Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR. Conclusion AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.
Collapse
|
28
|
Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect? Healthc Manage Forum 2023; 36:119-124. [PMID: 36226507 PMCID: PMC9976641 DOI: 10.1177/08404704221125368] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Artificial Intelligence (AI) for health has a great potential; it has already proven to be successful in enhancing patient outcomes, facilitating professional work and benefiting administration. However, AI presents challenges related to health equity defined as an opportunity for people to reach their fullest health potential. This article discusses the opportunities and challenges that AI presents in health and examines ways in which inequities related to AI can be mitigated.
Collapse
Affiliation(s)
| | | | - Christo El Morr
- York University, Toronto, Ontario, Canada.,Christo El Morr, York University, Toronto, Ontario, Canada. E-mail:
| |
Collapse
|
29
|
Morris MX, Song EY, Rajesh A, Asaad M, Phillips BT. Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery. Am Surg 2023; 89:55-60. [PMID: 35978473 DOI: 10.1177/00031348221117042] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.
Collapse
Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,22957Duke Pratt School of Engineering, Durham, NC, USA
| | - Ethan Y Song
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 14742Duke University Hospital, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 571198University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, 22957University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Brett T Phillips
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 14742Duke University Hospital, Durham, NC, USA
| |
Collapse
|
30
|
Al Meslamani AZ. How AI is advancing asthma management? Insights into economic and clinical aspects. J Med Econ 2023; 26:1489-1494. [PMID: 37902681 DOI: 10.1080/13696998.2023.2277072] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 10/26/2023] [Indexed: 10/31/2023]
Abstract
Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs worldwide. Artificial Intelligence (AI), particularly Machine Learning (ML), has been widely recognized as transformative when applied to asthma care. This commentary investigates how AI and ML may improve clinical outcomes while alleviating some of the costs associated with asthma care. AI's powerful analytical abilities could usher in an unprecedented era of preventive measures, particularly by identifying at-risk populations and anticipating environmental triggers. ML shows promise for enhancing real-time monitoring, early detection, and tailored treatment strategies in paediatric asthma, potentially reducing hospitalizations and emergency care costs. Emerging AI-powered wearable technologies are catalysing a revolutionary shift in patient monitoring, providing proactive interventions. Although optimistic, this commentary highlights a gap in empirical studies evaluating the cost-effectiveness of AI in asthma care and stresses the need for larger datasets to accurately represent the economic benefits of AI solutions. Additionally, this paper emphasizes the ethical considerations surrounding data privacy and algorithmic bias, which are vital for the successful and equitable integration of AI into healthcare settings. This editorial underscores the urgent necessity of conducting thorough analyses to assess all economic implications, facilitate optimized resource allocation, and foster a nuanced understanding of AI/ML technologies in asthma management that may reduce costs to healthcare systems.
Collapse
Affiliation(s)
- Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| |
Collapse
|
31
|
Wong DYL, Lam MC, Ran A, Cheung CY. Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions. Curr Opin Ophthalmol 2022; 33:440-446. [PMID: 35916571 DOI: 10.1097/icu.0000000000000886] [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: 11/26/2022]
Abstract
PURPOSE OF REVIEW Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.
Collapse
Affiliation(s)
- Dragon Y L Wong
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | | | | | | |
Collapse
|
32
|
Bhardwaj V, Sharma A, Parambath SV, Gul I, Zhang X, Lobie PE, Qin P, Pandey V. Machine Learning for Endometrial Cancer Prediction and Prognostication. Front Oncol 2022; 12:852746. [PMID: 35965548 PMCID: PMC9365068 DOI: 10.3389/fonc.2022.852746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
Collapse
Affiliation(s)
- Vipul Bhardwaj
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Arundhiti Sharma
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | | | - Ijaz Gul
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Xi Zhang
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peter E. Lobie
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peiwu Qin
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Vijay Pandey
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- *Correspondence: Vijay Pandey,
| |
Collapse
|
33
|
Gomez Rossi J, Feldberg B, Krois J, Schwendicke F. A systematic scoping review analysing clinical, technical and financial aspects of cost-effectiveness of Artificial Intelligence applied in medicine: A theory and framework of analysis (Preprint). JMIR Med Inform 2021; 10:e33703. [PMID: 35969458 PMCID: PMC9419048 DOI: 10.2196/33703] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 03/29/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Cost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology. Objective We aimed to systematically scope existing literature on the cost-effectiveness of AI and to extract and summarize clinical, technical, and economic dimensions required for a comprehensive assessment. Methods A scoping literature review was conducted to map medical, technical, and economic aspects considered in studies on the cost-effectiveness of medical AI. Based on these, a framework for health policy analysis was developed. Results Among 4820 eligible studies, 13 met the inclusion criteria for our review. Internal medicine and emergency medicine were the clinical disciplines most frequently analyzed. Most of the studies included were from the United States (5/13, 39%), assessed solutions requiring market access (9/13, 69%), and proposed optimization of direct resources as the most frequent value proposition (7/13, 53%). On the other hand, technical aspects were not uniformly disclosed in the studies we analyzed. A minority of articles explicitly stated the payment mechanism assumed (5/13, 38%), while it remained unspecified in the majority (8/13, 62%) of studies. Conclusions Current studies on the cost-effectiveness of AI do not allow to determine if the investigated AI solutions are clinically, technically, and economically viable. Further research and improved reporting on these dimensions seem relevant to recommend and assess potential use cases for this technology.
Collapse
Affiliation(s)
- Jesus Gomez Rossi
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Ben Feldberg
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| |
Collapse
|