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Dimitri P, Savage MO. Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 2024; 37:209-221. [PMID: 38183676 DOI: 10.1515/jpem-2023-0554] [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: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.
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Affiliation(s)
- Paul Dimitri
- Department of Paediatric Endocrinology, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Martin O Savage
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
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2
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Huang S, Liang Y, Li J, Li X. Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review. J Med Internet Res 2023; 25:e51024. [PMID: 38064249 PMCID: PMC10746969 DOI: 10.2196/51024] [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: 07/21/2023] [Revised: 11/10/2023] [Accepted: 11/12/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. OBJECTIVE This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. METHODS We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. RESULTS Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. CONCLUSIONS CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.37766/inplasy2022.9.0061.
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Affiliation(s)
- Shan Huang
- Endocrinology Department, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuzhen Liang
- Department of Endocrinology, The Second Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Jiarui Li
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou, China
| | - Xuejun Li
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Diabetes Institute, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
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3
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Unsworth R, Avari P, Lett AM, Oliver N, Reddy M. Adaptive bolus calculators for people with type 1 diabetes: A systematic review. Diabetes Obes Metab 2023; 25:3103-3113. [PMID: 37488945 DOI: 10.1111/dom.15204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/08/2023] [Accepted: 06/18/2023] [Indexed: 07/26/2023]
Abstract
AIM To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D). METHODS Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool. RESULTS Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration. CONCLUSIONS There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.
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Affiliation(s)
- Rebecca Unsworth
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Parizad Avari
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Aaron M Lett
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Monika Reddy
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
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4
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Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals. BMJ Open 2023; 13:e076017. [PMID: 37879682 PMCID: PMC10603416 DOI: 10.1136/bmjopen-2023-076017] [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] [Indexed: 10/27/2023] Open
Abstract
OBJECTIVE This work explores the perceptions of obstetrical clinicians about artificial intelligence (AI) in order to bridge the gap in uptake of AI between research and medical practice. Identifying potential areas where AI can contribute to clinical practice, enables AI research to align with the needs of clinicians and ultimately patients. DESIGN Qualitative interview study. SETTING A national study conducted in the Netherlands between November 2022 and February 2023. PARTICIPANTS Dutch clinicians working in obstetrics with varying relevant work experience, gender and age. ANALYSIS Thematic analysis of qualitative interview transcripts. RESULTS Thirteen gynaecologists were interviewed about hypothetical scenarios of an implemented AI model. Thematic analysis identified two major themes: perceived usefulness and trust. Usefulness involved AI extending human brain capacity in complex pattern recognition and information processing, reducing contextual influence and saving time. Trust required validation, explainability and successful personal experience. This result shows two paradoxes: first, AI is expected to provide added value by surpassing human capabilities, yet also a need to understand the parameters and their influence on predictions for trust and adoption was expressed. Second, participants recognised the value of incorporating numerous parameters into a model, but they also believed that certain contextual factors should only be considered by humans, as it would be undesirable for AI models to use that information. CONCLUSIONS Obstetricians' opinions on the potential value of AI highlight the need for clinician-AI researcher collaboration. Trust can be built through conventional means like randomised controlled trials and guidelines. Holistic impact metrics, such as changes in workflow, not just clinical outcomes, should guide AI model development. Further research is needed for evaluating evolving AI systems beyond traditional validation methods.
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Affiliation(s)
- Anne Fischer
- Department of Obstetrics and Gynecology, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Anna Rietveld
- Department of Obstetrics and Gynecology, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Pim Teunissen
- School of Health Professions Education, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Department of Gynaecology & Obstetrics, Maastricht UMC, Maastricht, The Netherlands
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Petra Bakker
- Department of Obstetrics and Gynecology, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
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5
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Yoo JH, Kim JH. Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control. Diabetes Metab J 2023; 47:27-41. [PMID: 36635028 PMCID: PMC9925143 DOI: 10.4093/dmj.2022.0271] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/28/2022] [Indexed: 01/14/2023] Open
Abstract
Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.
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Affiliation(s)
- Jee Hee Yoo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae Hyeon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea
- Corresponding author: Jae Hyeon Kim https://orcid.org/0000-0001-5001-963X Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea E-mail:
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6
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Nimri R, Tirosh A, Muller I, Shtrit Y, Kraljevic I, Alonso MM, Milicic T, Saboo B, Deeb A, Christoforidis A, den Brinker M, Bozzetto L, Bolla AM, Krcma M, Rabini RA, Tabba S, Gerasimidi-Vazeou A, Maltoni G, Giani E, Dotan I, Liberty IF, Toledano Y, Kordonouri O, Bratina N, Dovc K, Biester T, Atlas E, Phillip M. Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy. Diabetes Technol Ther 2022; 24:564-572. [PMID: 35325567 DOI: 10.1089/dia.2021.0566] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo.Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.
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Affiliation(s)
- Revital Nimri
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
| | - Amir Tirosh
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
- Division of Endocrinology, Diabetes and Metabolism, Dalia and David Arabov Endocrinology and Diabetes Research Center, Sheba Medical Center, Tel Hashomer, Israel
| | - Ido Muller
- DreaMed Diabetes Ltd., Petah Tikva, Israel
| | | | - Ivana Kraljevic
- Department of Endocrinology and Diabetes, UHC Zagreb, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Montserrat Martín Alonso
- Department of Pediatrics, Children's Endocrinology Unit, University Hospital of Salamanca, Spain
| | - Tanja Milicic
- Clinic for Endocrinology, Diabetes and Metabolic Diseases, University Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Banshi Saboo
- Dia Care Diabetes Care and Hormone Clinic, Ahmedabad, Gujarat, India
| | - Asma Deeb
- Pediatric Endocrine Division, Sheikh Shakhbout Medical City and Khalifa University, Abu Dhabi, United Arab Emirates
| | - Athanasios Christoforidis
- 1st Pediatric Department, Aristotle University of Thessaloniki, Hippokratio General Hospital, Thessaloniki, Greece
| | - Marieke den Brinker
- Division of Pediatric Endocrinology and Diabetology, Department of Pediatrics, Antwerp University Hospital and University of Antwerp, Antwerpen, Belgium
| | - Lutgarda Bozzetto
- Department of Clinical Medicine and Surgery, University of Naples "Federico II," Naples, Italy
| | | | - Michal Krcma
- Diabetes and Endocrinology Unit, Department of Internal Medicine, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
| | - Rosa Anna Rabini
- Department of Diabetology, Hospital Mazzoni, Ascoli Piceno, Italy
| | - Shadi Tabba
- Department of Pediatric Endocrinology, Arnold Palmer Hospital for Children, Orlando, Florida, USA
| | | | - Giulio Maltoni
- Unit of Pediatrics, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Elisa Giani
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Idit Dotan
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
- Rabin Medical Center, Institute of Endocrinology, Beilinson Hospital, Petach Tikva, Israel
| | - Idit F Liberty
- Department of Medicine and Diabetes Unit, Soroka Medical Center, Faculty of Health Sciences, Beer Sheva, Israel
| | - Yoel Toledano
- Division of Maternal Fetal Medicine, Helen Schneider Women's Hospital, Rabin Medical Center, Endocrinology Clinic, Petah Tikva, Israel
| | - Olga Kordonouri
- Diabetes Center for Children and Adolescents, Children's Hospital AUF DER BULT, Hannover, Germany
| | - Natasa Bratina
- Department of Endocrinology, Diabetes and Metabolic Diseases, UMC-University Children's Hospital Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Klemen Dovc
- Department of Endocrinology, Diabetes and Metabolic Diseases, UMC-University Children's Hospital Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Torben Biester
- Diabetes Center for Children and Adolescents, Children's Hospital AUF DER BULT, Hannover, Germany
| | - Eran Atlas
- DreaMed Diabetes Ltd., Petah Tikva, Israel
| | - Moshe Phillip
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Hashomer, Israel
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7
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Xu NY, Nguyen KT, DuBord AY, Pickup J, Sherr JL, Teymourian H, Cengiz E, Ginsberg BH, Cobelli C, Ahn D, Bellazzi R, Bequette BW, Gandrud Pickett L, Parks L, Spanakis EK, Masharani U, Akturk HK, Melish JS, Kim S, Kang GE, Klonoff DC. Diabetes Technology Meeting 2021. J Diabetes Sci Technol 2022; 16:1016-1056. [PMID: 35499170 PMCID: PMC9264449 DOI: 10.1177/19322968221090279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.
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Affiliation(s)
- Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | | | | | | | | | | | - Eda Cengiz
- University of California, San
Francisco, San Francisco, CA, USA
| | | | | | - David Ahn
- Mary & Dick Allen Diabetes Center
at Hoag, Newport Beach, CA, USA
| | | | | | | | - Linda Parks
- University of California, San
Francisco, San Francisco, CA, USA
| | - Elias K. Spanakis
- Baltimore VA Medical Center,
Baltimore, MD, USA
- University of Maryland, Baltimore,
MD, USA
| | - Umesh Masharani
- University of California, San
Francisco, San Francisco, CA, USA
| | - Halis K. Akturk
- Barbara Davis Center for Diabetes,
University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Sarah Kim
- University of California, San
Francisco, San Francisco, CA, USA
| | - Gu Eon Kang
- The University of Texas at Dallas,
Richardson, TX, USA
| | - David C. Klonoff
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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8
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Melvin RL, Broyles MG, Duggan EW, John S, Smith AD, Berkowitz DE. Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices. Front Digit Health 2022; 4:872675. [PMID: 35547090 PMCID: PMC9081677 DOI: 10.3389/fdgth.2022.872675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/29/2022] [Indexed: 11/28/2022] Open
Abstract
As implementation of artificial intelligence grows more prevalent in perioperative medicine, a clinician's ability to distinguish differentiating aspects of these algorithms is critical. There are currently numerous marketing and technical terms to describe these algorithms with little standardization. Additionally, the need to communicate with algorithm developers is paramount to actualize effective and practical implementation. Of particular interest in these discussions is the extent to which the output or predictions of algorithms and tools are understandable by medical practitioners. This work proposes a simple nomenclature that is intelligible to both clinicians and developers for quickly describing the interpretability of model results. There are three high-level categories: transparent, translucent, and opaque. To demonstrate the applicability and utility of this terminology, these terms were applied to the artificial intelligence and machine-learning-based products that have gained Food and Drug Administration approval. During this review and categorization process, 22 algorithms were found with perioperative utility (in a database of 70 total algorithms), and 12 of these had publicly available citations. The primary aim of this work is to establish a common nomenclature that will expedite and simplify descriptions of algorithm requirements from clinicians to developers and explanations of appropriate model use and limitations from developers to clinicians.
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Affiliation(s)
- Ryan L. Melvin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Ryan L. Melvin
| | - Matthew G. Broyles
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Elizabeth W. Duggan
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sonia John
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Andrew D. Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dan E. Berkowitz
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
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9
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Shang T, Zhang JY, Bequette BW, Raymond JK, Coté G, Sherr JL, Castle J, Pickup J, Pavlovic Y, Espinoza J, Messer LH, Heise T, Mendez CE, Kim S, Ginsberg BH, Masharani U, Galindo RJ, Klonoff DC. Diabetes Technology Meeting 2020. J Diabetes Sci Technol 2021; 15:916-960. [PMID: 34196228 PMCID: PMC8258529 DOI: 10.1177/19322968211016480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.
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Affiliation(s)
- Trisha Shang
- Diabetes Technology Society, Burlingame, CA, USA
| | | | | | - Jennifer K. Raymond
- Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | - Gerard Coté
- Texas A & M University, College Station, Texas, USA
| | | | | | | | | | - Juan Espinoza
- Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Sarah Kim
- University of California San Francisco, San Francisco, CA, USA
| | | | - Umesh Masharani
- University of California San Francisco, San Francisco, CA, USA
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