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Alhalafi A, Alqahtani SM, Alqarni NA, Aljuaid AT, Aljaber GT, Alshahrani LM, Mushait H, Nandi PA. Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis. Cureus 2024; 16:e58713. [PMID: 38779284 PMCID: PMC11110080 DOI: 10.7759/cureus.58713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 04/21/2024] [Indexed: 05/25/2024] Open
Abstract
Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.
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Affiliation(s)
- Abdullah Alhalafi
- Department of Family and Community Medicine, University of Bisha, Bisha, SAU
| | | | | | | | - Ghade T Aljaber
- Department of Medicine, Batterjee Medical College, Aseer, SAU
| | | | | | - Partha A Nandi
- Department of Family and Community Medicine, University of Bisha, Bisha, SAU
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Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, Ananthakrishnan A, Brown EA, Prichett L, Lehmann HP, Abramoff MD. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun 2024; 15:421. [PMID: 38212308 PMCID: PMC10784572 DOI: 10.1038/s41467-023-44676-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: 06/16/2023] [Accepted: 12/21/2023] [Indexed: 01/13/2024] Open
Abstract
Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.
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Affiliation(s)
- Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - T Y Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anum Zehra
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lee Bromberger
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dhruva Patel
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Elizabeth A Brown
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Laura Prichett
- Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, MD, USA
| | - Harold P Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
- Digital Diagnostics Inc, Coralville, IA, USA
- Iowa City VA Medical Center, Iowa City, IA, USA
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
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Abramoff MD, Whitestone N, Patnaik JL, Rich E, Ahmed M, Husain L, Hassan MY, Tanjil MSH, Weitzman D, Dai T, Wagner BD, Cherwek DH, Congdon N, Islam K. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. NPJ Digit Med 2023; 6:184. [PMID: 37794054 PMCID: PMC10550906 DOI: 10.1038/s41746-023-00931-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/20/2023] [Indexed: 10/06/2023] Open
Abstract
Autonomous artificial intelligence (AI) promises to increase healthcare productivity, but real-world evidence is lacking. We developed a clinic productivity model to generate testable hypotheses and study design for a preregistered cluster-randomized clinical trial, in which we tested the hypothesis that a previously validated US FDA-authorized AI for diabetic eye exams increases clinic productivity (number of completed care encounters per hour per specialist physician) among patients with diabetes. Here we report that 105 clinic days are cluster randomized to either intervention (using AI diagnosis; 51 days; 494 patients) or control (not using AI diagnosis; 54 days; 499 patients). The prespecified primary endpoint is met: AI leads to 40% higher productivity (1.59 encounters/hour, 95% confidence interval [CI]: 1.37-1.80) than control (1.14 encounters/hour, 95% CI: 1.02-1.25), p < 0.00; the secondary endpoint (productivity in all patients) is also met. Autonomous AI increases healthcare system productivity, which could potentially increase access and reduce health disparities. ClinicalTrials.gov NCT05182580.
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Affiliation(s)
- Michael D Abramoff
- University of Iowa, Iowa City, Iowa, USA.
- Digital Diagnostics Inc, Coralville, Iowa, USA.
- Iowa City Veterans Affairs Medical Center, Iowa City, Iowa, USA.
- Department of Biomedical Engineering, The University of Iowa, Iowa City, USA.
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, USA.
| | | | - Jennifer L Patnaik
- Orbis International, New York, New York, USA
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Emily Rich
- Orbis International, New York, New York, USA
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | | | | | | | | | | | - Tinglong Dai
- Carey Business School, Johns Hopkins University, Baltimore, Maryland, USA
- Hopkins Business of Health Initiative, Johns Hopkins University, Baltimore, Maryland, USA
- School of Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brandie D Wagner
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | | | - Nathan Congdon
- Orbis International, New York, New York, USA
- Centre for Public Health, Queen's University Belfast, Belfast, UK
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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Wolf RM, Channa R, Lehmann HP, Abramoff MD, Liu TA. Clinical Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success. Clin Diabetes 2023; 42:142-149. [PMID: 38230333 PMCID: PMC10788651 DOI: 10.2337/cd23-0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Affiliation(s)
- Risa M. Wolf
- Department of Pediatric Endocrinology and Diabetes, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI
| | - Harold P. Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD
| | - Michael D. Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA
- Digital Diagnostics, Coralville, IA
| | - T.Y. Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD
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Spital G, Faatz H. Diabetic Retinopathy - a Common Disease. Klin Monbl Augenheilkd 2023; 240:1060-1070. [PMID: 37666252 DOI: 10.1055/a-2108-6758] [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: 09/06/2023]
Abstract
Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus and one of the leading causes of visual impairment in working age individuals in the western world. The treatment of DR depends on its severity, so it is of great importance to detect patients as early as possible, in order to initiate early treatment and preserve vision. Despite currently insufficient screening participation, patients with diabetes already visit ophthalmological practices and clinics above average. Their medical care, including DR diagnostics and treatment has been making up an increasing proportion of ophthalmic activity for years. Since the prevalence of diabetes is increasing dramatically worldwide and a further increase is also predicted for Germany, the challenge for ophthalmologists is likely to grow considerably. As the same time, the diagnostic possibilities for differentiating DR and the therapeutic measures, especially with IVOM therapy, are becoming more and more complex, which increases the time burden in everyday clinical practice. The hope to avoid healthcare deficits and to further improve screening rates and visual acuity prognosis in patients with DR is based, among other things, on camera-assisted screening supported by artificial intelligence. Better diabetes management to reduce the prevalence of DR, as well as longer-acting drugs to treat DR, could also improve the care and help reduce the burden on ophthalmology practices.
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Affiliation(s)
- Georg Spital
- Augenzentrum am St. Franziskus-Hospital, Münster, Deutschland
| | - Henrik Faatz
- Augenzentrum am St. Franziskus-Hospital, Münster, Deutschland
- Achim-Wessing-Institut für Ophthalmologische Bildgebung, Universität Essen, Deutschland
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