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Sachedina D, Hooda R, Fawaz B. Practical applications of artificial intelligence in dermatology residency training. Clin Exp Dermatol 2024; 49:925-926. [PMID: 38499510 DOI: 10.1093/ced/llae096] [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: 09/02/2023] [Revised: 02/19/2024] [Accepted: 03/14/2024] [Indexed: 03/20/2024]
Abstract
Artificial intelligence (AI) is revolutionizing healthcare, notably in dermatology diagnostics using deep learning, such as convolutional neural networks. This piece spotlights AI’s potential in dermatology training: (i) enhancing academic training with tailored, interactive learning; (ii) boosting surgical proficiency via virtual reality and real-time AI feedback; and (iii) customizing training through AI-identified clinical gaps. However, integrating AI requires substantial investments, a paradigm shift in teaching methods, and an understanding of the evolving dynamics of dermatological practices as AI becomes integral.
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Affiliation(s)
| | - Rohan Hooda
- NHS England, Redditch, UK
- Department of Dermatology, Boston Medical Center, Boston, MA, USA
| | - Bilal Fawaz
- Department of Dermatology, Boston Medical Center, Boston, MA, USA
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2
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Potestio L, Megna M, Cacciapuoti S, Martora F, Villani A. ChatGPT and medical writing in dermatology: why should we keep writing? Clin Exp Dermatol 2024; 49:929-930. [PMID: 38517046 DOI: 10.1093/ced/llae105] [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: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 03/23/2024]
Abstract
The incorporation of artificial intelligence into routine dermatological clinical procedures is experiencing a significant upward trend. In the dermatological field, ChatGPT may change medical writing by powering certain tasks and improving the efficiency of the writing process.
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Affiliation(s)
- Luca Potestio
- Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Matteo Megna
- Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Sara Cacciapuoti
- Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Fabrizio Martora
- Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Alessia Villani
- Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
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3
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Capurro N, Pastore VP, Touijer L, Odone F, Cozzani E, Gasparini G, Parodi A. A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases. Br J Dermatol 2024; 191:261-266. [PMID: 38581445 DOI: 10.1093/bjd/ljae142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/19/2024] [Accepted: 03/29/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is reshaping healthcare, using machine and deep learning (DL) to enhance disease management. Dermatology has seen improved diagnostics, particularly in skin cancer detection, through the integration of AI. However, the potential of AI in automating immunofluorescence imaging for autoimmune bullous skin diseases (AIBDs) remains untapped. While direct immunofluorescence (DIF) supports diagnosis, its manual interpretation can hinder efficiency. The use of DL to classify DIF patterns automatically, including the intercellular (ICP) and linear pattern (LP), holds promise for improving the diagnosis of AIBDs. OBJECTIVES To develop AI algorithms for automated classification of AIBD DIF patterns, such as ICP and LP, in order to enhance diagnostic accuracy, streamline disease management and improve patient outcomes through DL-driven immunofluorescence interpretation. METHODS We collected immunofluorescence images from skin biopsies of patients suspected of having an AIBD between January 2022 and January 2024. Skin tissue was obtained via a 5-mm punch biopsy, prepared for DIF. Experienced dermatologists classified the images as ICP, LP or negative. To evaluate our DL approach, we divided the images into training (n = 436) and test sets (n = 93). We employed transfer learning with pretrained deep neural networks and conducted fivefold cross-validation to assess model performance. Our dataset's class imbalance was addressed using weighted loss and data augmentation strategies. The models were trained for 50 epochs using Pytorch, achieving an image size of 224 × 224 pixels for both convolutional neural networks (CNNs) and the Swin Transformer. RESULTS Our study compared six CNNs and the Swin Transformer for AIBD image classification, with the Swin Transformer achieving the highest average validation accuracy (98.5%). On a separate test set, the best model attained an accuracy of 94.6%, demonstrating 95.3% sensitivity and 97.5% specificity across AIBD classes. Visualization with Grad-CAM (class activation mapping) highlighted the model's reliance on characteristic patterns for accurate classification. CONCLUSIONS The study highlighted the accuracy of CNNs in identifying DIF features. This approach aids automated analysis and reporting, offering reproducibility, speed, data handling and cost-efficiency. Integrating DL into skin immunofluorescence promises precise diagnostics and streamlined reporting in this branch of dermatology.
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Affiliation(s)
- Niccolò Capurro
- Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy
| | | | | | | | - Emanuele Cozzani
- Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Giulia Gasparini
- Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Aurora Parodi
- Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy
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4
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Doolan BJ, Thomas BR. Bursting the bubble on diagnostics: artificial intelligence in autoimmune bullous disease. Br J Dermatol 2024; 191:160-161. [PMID: 38736238 DOI: 10.1093/bjd/ljae197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 03/13/2024] [Indexed: 05/14/2024]
Affiliation(s)
- Brent J Doolan
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
- St John's Institute of Dermatology, Guy's and St Thomas' Hospital, London, UK
| | - Bjorn R Thomas
- St John's Institute of Dermatology, Guy's and St Thomas' Hospital, London, UK
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5
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Goldust M, Grant-Kels JM. Using AI to help address skin health challenges caused by climate change. Int J Dermatol 2024; 63:e126-e127. [PMID: 38703181 DOI: 10.1111/ijd.17222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Mohamad Goldust
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Jane M Grant-Kels
- Dermatology Department, University of Connecticut School of Medicine, Farmington, CT, USA
- Dermatology Department, University of Florida, College of Medicine, Gainesville, FL, USA
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6
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Goldust M. Utilizing AI to address skin disorders and healthcare disparities among undocumented immigrants. Int J Dermatol 2024; 63:e123. [PMID: 38647192 DOI: 10.1111/ijd.17208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Affiliation(s)
- Mohamad Goldust
- Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, USA
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7
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Lewandowski M, Łukowicz P, Świetlik D, Barańska-Rybak W. ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology. Clin Exp Dermatol 2024; 49:686-691. [PMID: 37540015 DOI: 10.1093/ced/llad255] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/17/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND The global use of artificial intelligence (AI) has the potential to revolutionize the healthcare industry. Despite the fact that AI is becoming more popular, there is still a lack of evidence on its use in dermatology. OBJECTIVES To determine the capacity of ChatGPT-3.5 and ChatGPT-4 to support dermatology knowledge and clinical decision-making in medical practice. METHODS Three Specialty Certificate Examination in Dermatology tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice questions each, were used to assess the performance of ChatGPT-3.5 and ChatGPT-4. RESULTS ChatGPT-4 exceeded the 60% pass rate in every performed test, with a minimum of 80% and 70% correct answers for the English and Polish versions, respectively. ChatGPT-4 performed significantly better on each exam (P < 0.01), regardless of language, compared with ChatGPT-3.5. Furthermore, ChatGPT-4 answered clinical picture-type questions with an average accuracy of 93.0% and 84.2% for questions in English and Polish, respectively. The difference between the tests in Polish and English were not significant; however, ChatGPT-3.5 and ChatGPT-4 performed better overall in English than in Polish by an average of 8 percentage points for each test. Incorrect ChatGPT answers were highly correlated with a lower difficulty index, denoting questions of higher difficulty in most of the tests (P < 0.05). CONCLUSIONS The dermatology knowledge level of ChatGPT was high, and ChatGPT-4 performed significantly better than ChatGPT-3.5. Although the use of ChatGPT will not replace a doctor's final decision, physicians should support the development of AI in dermatology to raise the standards of medical care.
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Affiliation(s)
- Miłosz Lewandowski
- Department of Dermatology, Venereology and Allergology, Faculty of Medicine
| | - Paweł Łukowicz
- Division of Biostatistics and Neural Networks, Medical University of Gdansk, Gdansk, Poland
| | - Dariusz Świetlik
- Division of Biostatistics and Neural Networks, Medical University of Gdansk, Gdansk, Poland
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8
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Trager MH, Gordon ER, Breneman A, Weng C, Samie FH. Artificial intelligence for nonmelanoma skin cancer. Clin Dermatol 2024:S0738-081X(24)00100-7. [PMID: 38925444 DOI: 10.1016/j.clindermatol.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.
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Affiliation(s)
- Megan H Trager
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Alyssa Breneman
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Faramarz H Samie
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA.
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Fava ALM, de Souza CM, dos Santos ÉM, Silvério LAL, Ataide JA, Paiva-Santos AC, Costa JL, de Melo DO, Mazzola PG. Evidence of Cannabidiol Effectiveness Associated or Not with Tetrahydrocannabinol in Topical Administration: A Scope Review. Pharmaceuticals (Basel) 2024; 17:748. [PMID: 38931415 PMCID: PMC11206585 DOI: 10.3390/ph17060748] [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: 05/09/2024] [Revised: 05/31/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024] Open
Abstract
Cannabis sativa is a plant of the Cannabaceae family, whose molecular composition is known for its vast pharmacological properties. Cannabinoids are the molecules responsible for Cannabis sativa potential effects, especially tetrahydrocannabinol and cannabidiol. Scientific development has shown interest in the potential of cannabidiol in various health conditions, as it has demonstrated lower adverse events and great pharmacological potential, especially when administered topically. The present study aims to carry out a scoping review, focusing on the use of cannabidiol, in vivo models, for topical administration. Thus, the methodological approach used by the Joanna Briggs Institute was applied, and the studies were selected based on previously established inclusion criteria. Even though more information regarding the dose to achieve pharmacological potential is still needed, cannabidiol demonstrated potential in treating and preventing different conditions, such as glaucoma, atopic dermatitis, epidermolysis bullosa, and pyoderma gangrenosum.
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Affiliation(s)
- Ana Laura Masquetti Fava
- Universidade Estadual de Campinas (UNICAMP), Faculdade de Ciências Médicas, Campinas 13083-887, Brazil
| | - Cinthia Madeira de Souza
- Universidade Estadual de Campinas (UNICAMP), Faculdade de Ciências Médicas, Campinas 13083-887, Brazil
| | - Érica Mendes dos Santos
- Universidade Estadual de Campinas (UNICAMP), Faculdade de Ciências Farmacêuticas, Campinas 13083-871, Brazil
| | | | - Janaína Artem Ataide
- Universidade Estadual de Campinas (UNICAMP), Faculdade de Ciências Farmacêuticas, Campinas 13083-871, Brazil
| | - Ana Cláudia Paiva-Santos
- Department of Pharmaceutical Technology, Faculty of Pharmacy of the University of Coimbra, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
- REQUIMTE/LAQV, Department of Pharmaceutical Technology, Faculty of Pharmacy of the University of Coimbra, University of Coimbra, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal
| | - Jose Luiz Costa
- Universidade Estadual de Campinas (UNICAMP), Faculdade de Ciências Farmacêuticas, Campinas 13083-871, Brazil
- Centro de Informação e Assistência Toxicológica de Campinas, Faculdade de Ciências Médicas, Universidade Estadual de Campinas (UNICAMP), Campinas 13083-970, Brazil
| | - Daniela Oliveira de Melo
- Departamento de Ciências Farmacêuticas, Instituto de Ciências Ambientais, Químicas e Farmacêuticas, Universidade Federal de São Paulo (UNIFESP), Diadema 09972-270, Brazil
| | - Priscila Gava Mazzola
- Universidade Estadual de Campinas (UNICAMP), Faculdade de Ciências Farmacêuticas, Campinas 13083-871, Brazil
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10
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Gordon ER, Trager MH, Kontos D, Weng C, Geskin LJ, Dugdale LS, Samie FH. Ethical considerations for artificial intelligence in dermatology: a scoping review. Br J Dermatol 2024; 190:789-797. [PMID: 38330217 DOI: 10.1093/bjd/ljae040] [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: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.
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Affiliation(s)
- Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Megan H Trager
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Despina Kontos
- University of Pennsylvania, Perelman School of Medicine, Department of Radiology, Philadelphia, PA, USA
- Radiology
| | | | - Larisa J Geskin
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Lydia S Dugdale
- Columbia University Vagelos College of Physicians and Surgeons, Department of Medicine, Center for Clinical Medical Ethics, New York, NY, USA
| | - Faramarz H Samie
- Columbia University Irving Medical Center, Departments of Dermatology
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Mashoudy KD, Perez SM, Nouri K. From diagnosis to intervention: a review of telemedicine's role in skin cancer care. Arch Dermatol Res 2024; 316:139. [PMID: 38696032 PMCID: PMC11065900 DOI: 10.1007/s00403-024-02884-7] [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: 01/11/2024] [Revised: 04/03/2024] [Accepted: 04/14/2024] [Indexed: 05/05/2024]
Abstract
Skin cancer treatment is a core aspect of dermatology that relies on accurate diagnosis and timely interventions. Teledermatology has emerged as a valuable asset across various stages of skin cancer care including triage, diagnosis, management, and surgical consultation. With the integration of traditional dermoscopy and store-and-forward technology, teledermatology facilitates the swift sharing of high-resolution images of suspicious skin lesions with consulting dermatologists all-over. Both live video conference and store-and-forward formats have played a pivotal role in bridging the care access gap between geographically isolated patients and dermatology providers. Notably, teledermatology demonstrates diagnostic accuracy rates that are often comparable to those achieved through traditional face-to-face consultations, underscoring its robust clinical utility. Technological advancements like artificial intelligence and reflectance confocal microscopy continue to enhance image quality and hold potential for increasing the diagnostic accuracy of virtual dermatologic care. While teledermatology serves as a valuable clinical tool for all patient populations including pediatric patients, it is not intended to fully replace in-person procedures like Mohs surgery and other necessary interventions. Nevertheless, its role in facilitating the evaluation of skin malignancies is gaining recognition within the dermatologic community and fostering high approval rates from patients due to its practicality and ability to provide timely access to specialized care.
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Affiliation(s)
- Kayla D Mashoudy
- University of Miami Miller School of Medicine, 1600 NW 10th Ave #1140, Miami, FL, 33136, USA.
| | - Sofia M Perez
- University of Miami Miller School of Medicine, 1600 NW 10th Ave #1140, Miami, FL, 33136, USA
| | - Keyvan Nouri
- Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, 1150 NW 14th Street, Miami, FL, 33136, USA
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12
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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
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13
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Yu Z, Flament F, Jiang R, Houghton J, Kroely C, Cabut N, Haykal D, Sehgal C, Jablonski NG, Jean A, Aarabi P. The relevance and accuracy of an AI algorithm-based descriptor on 23 facial attributes in a diverse female US population. Skin Res Technol 2024; 30:e13690. [PMID: 38716749 PMCID: PMC11077572 DOI: 10.1111/srt.13690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/18/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The response of AI in situations that mimic real life scenarios is poorly explored in populations of high diversity. OBJECTIVE To assess the accuracy and validate the relevance of an automated, algorithm-based analysis geared toward facial attributes devoted to the adornment routines of women. METHODS In a cross-sectional study, two diversified groups presenting similar distributions such as age, ancestry, skin phototype, and geographical location was created from the selfie images of 1041 female in a US population. 521 images were analyzed as part of a new training dataset aimed to improve the original algorithm and 520 were aimed to validate the performance of the AI. From a total 23 facial attributes (16 continuous and 7 categorical), all images were analyzed by 24 make-up experts and by the automated descriptor tool. RESULTS For all facial attributes, the new and the original automated tool both surpassed the grading of the experts on a diverse population of women. For the 16 continuous attributes, the gradings obtained by the new system strongly correlated with the assessment made by make-up experts (r ≥ 0.80; p < 0.0001) and supported by a low error rate. For the seven categorical attributes, the overall accuracy of the AI-facial descriptor was improved via enrichment of the training dataset. However, some weaker performance in spotting specific facial attributes were noted. CONCLUSION In conclusion, the AI-automatic facial descriptor tool was deemed accurate for analysis of facial attributes for diverse women although some skin complexion, eye color, and hair features required some further finetuning.
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Affiliation(s)
- Zhi Yu
- Modiface – A L'Oréal Group CompanyTorontoCanada
| | | | | | | | | | | | | | | | - Nina G Jablonski
- Department of AnthropologyThe Pennsylvania State University, University ParkPennsylvaniaUSA
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14
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Grzybowski A, Jin K, Wu H. Challenges of artificial intelligence in medicine and dermatology. Clin Dermatol 2024; 42:210-215. [PMID: 38184124 DOI: 10.1016/j.clindermatol.2023.12.013] [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: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Kai Jin
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongkang Wu
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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15
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Alhatem A, Wong T, Clark Lambert W. Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence-what doesn't kill you makes you stronger? Clin Dermatol 2024; 42:268-274. [PMID: 38181890 DOI: 10.1016/j.clindermatol.2023.12.020] [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: 01/07/2024]
Abstract
This study explored the integration and impact of artificial intelligence (AI) in diagnostic pathology, particularly dermatopathology, assessing its challenges and potential solutions for global health care enhancement. A comprehensive literature search in PubMed and Google Scholar, conducted on March 30, 2023, and using terms related to AI, pathology, and machine learning, yielded 44 relevant publications. These were analyzed under themes including the evolution of deep learning in pathology, AI's role in replacing pathologists, development challenges of diagnostic algorithms, clinical implementation hurdles, strategies for practical application in dermatopathology, and future prospects of AI in this field. The findings highlight AI's transformative potential in pathology, underscore the need for ongoing research, collaboration, and regulatory dialogue, and emphasize the importance of addressing the ethical and practical challenges in AI implementation for improved global health care outcomes.
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Affiliation(s)
- Albert Alhatem
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - Trish Wong
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - W Clark Lambert
- Department of Pathology, Immunology and Laboratory Medicine and Department of Dermatology, Rutgers-New Jersey Medical School, Newark, New Jersey, USA.
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16
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Chatzilakou E, Hu Y, Jiang N, Yetisen AK. Biosensors for melanoma skin cancer diagnostics. Biosens Bioelectron 2024; 250:116045. [PMID: 38301546 DOI: 10.1016/j.bios.2024.116045] [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: 10/20/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 02/03/2024]
Abstract
Skin cancer is a critical global public health concern, with melanoma being the deadliest variant, correlated to 80% of skin cancer-related deaths and a remarkable propensity to metastasize. Despite notable progress in skin cancer prevention and diagnosis, the limitations of existing methods accentuate the demand for precise diagnostic tools. Biosensors have emerged as valuable clinical tools, enabling rapid and reliable point-of-care (POC) testing of skin cancer. This review offers insights into skin cancer development, highlights essential cutaneous melanoma biomarkers, and assesses the current landscape of biosensing technologies for diagnosis. The comprehensive analysis in this review underscores the transformative potential of biosensors in revolutionizing melanoma skin cancer diagnosis, emphasizing their critical role in advancing patient outcomes and healthcare efficiency. The increasing availability of these approaches supports direct diagnosis and aims to reduce the reliance on biopsies, enhancing POC diagnosis. Recent advancements in biosensors for skin cancer diagnosis hold great promise, with their integration into healthcare expected to enhance early detection accuracy and reliability, thereby mitigating socioeconomic disparities.
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Affiliation(s)
- Eleni Chatzilakou
- Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2BU, UK
| | - Yubing Hu
- Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2BU, UK.
| | - Nan Jiang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China; JinFeng Laboratory, Chongqing, 401329, China.
| | - Ali K Yetisen
- Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2BU, UK.
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17
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Gefeller O, Kaiser I, Brockmann EM, Uter W, Pfahlberg AB. The Level of Agreement between Self-Assessments and Examiner Assessments of Melanocytic Nevus Counts: Findings from an Evaluation of 4548 Double Assessments. Curr Oncol 2024; 31:2221-2232. [PMID: 38668067 PMCID: PMC11048774 DOI: 10.3390/curroncol31040164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Cutaneous melanoma (CM) is a candidate for screening programs because its prognosis is excellent when diagnosed at an early disease stage. Targeted screening of those at high risk for developing CM, a cost-effective alternative to population-wide screening, requires valid procedures to identify the high-risk group. Self-assessment of the number of nevi has been suggested as a component of such procedures, but its validity has not yet been established. We analyzed the level of agreement between self-assessments and examiner assessments of the number of melanocytic nevi in the area between the wrist and the shoulder of both arms based on 4548 study subjects in whom mutually blinded double counting of nevi was performed. Nevus counting followed the IARC protocol. Study subjects received written instructions, photographs, a mirror, and a "nevometer" to support self-assessment of nevi larger than 2 mm. Nevus counts were categorized based on the quintiles of the distribution into five levels, defining a nevus score. Cohen's weighted kappa coefficient (κ) was estimated to measure the level of agreement. In the total sample, the agreement between self-assessments and examiner assessments was moderate (weighted κ = 0.596). Self-assessed nevus counts were higher than those determined by trained examiners (mean difference: 3.33 nevi). The level of agreement was independent of sociodemographic and cutaneous factors; however, participants' eye color had a significant impact on the level of agreement. Our findings show that even with comprehensive guidance, only a moderate level of agreement between self-assessed and examiner-assessed nevus counts can be achieved. Self-assessed nevus information does not appear to be reliable enough to be used in individual risk assessment to target screening activities.
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Affiliation(s)
- Olaf Gefeller
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (I.K.); (W.U.); (A.B.P.)
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18
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Krakowski I, Kim J, Cai ZR, Daneshjou R, Lapins J, Eriksson H, Lykou A, Linos E. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:78. [PMID: 38594408 PMCID: PMC11004168 DOI: 10.1038/s41746-024-01031-w] [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: 09/22/2023] [Accepted: 02/05/2024] [Indexed: 04/11/2024] Open
Abstract
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
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Affiliation(s)
- Isabelle Krakowski
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Jiyeong Kim
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Zhuo Ran Cai
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Roxana Daneshjou
- Department of Dermatology, Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Jan Lapins
- Department of Dermatology, Theme Inflammation, Karolinska University Hospital, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Unit of Head-Neck-, Lung- and Skin Cancer, Skin Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Anastasia Lykou
- Department of Education, University of Nicosia, Nicosia, Cyprus
| | - Eleni Linos
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA.
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19
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Lasheras-Pérez MA, Taberner R, Martínez-Jarreta B. Bioethical Conflicts in Current Dermatology: A Narrative Review. ACTAS DERMO-SIFILIOGRAFICAS 2024:S0001-7310(24)00264-3. [PMID: 38556205 DOI: 10.1016/j.ad.2024.02.031] [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: 09/20/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Both the functions and equipment of dermatologists have increased over the past few years, some examples being cosmetic dermatology, artificial intelligence, tele-dermatology, and social media, which added to the pharmaceutical industry and cosmetic selling has become a source of bioethical conflicts. The objective of this narrative review is to identify the bioethical conflicts of everyday dermatology practice and highlight the proposed solutions. Therefore, we conducted searches across PubMed, Web of Science and Scopus databases. Also, the main Spanish and American deontological codes of physicians and dermatologists have been revised. The authors recommend declaring all conflicts of interest while respecting the patients' autonomy, confidentiality, and privacy. Cosmetic dermatology, cosmetic selling, artificial intelligence, tele-dermatology, and social media are feasible as long as the same standards of conventional dermatology are applied. Nonetheless, the deontological codes associated with these innovations need to be refurbished.
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Affiliation(s)
- M A Lasheras-Pérez
- Servicios de Dermatología, Hospital Universitario y Politécnico la Fe, Valencia, España
| | - R Taberner
- Unidad de Dermatología, Hospital Universitari Son Llàtzer, Palma de Mallorca, España.
| | - B Martínez-Jarreta
- Departamento de Medicina Legal y Forense, Universidad de Zaragoza, Zaragoza, España
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20
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Wei ML, Tada M, So A, Torres R. Artificial intelligence and skin cancer. Front Med (Lausanne) 2024; 11:1331895. [PMID: 38566925 PMCID: PMC10985205 DOI: 10.3389/fmed.2024.1331895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.
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Affiliation(s)
- Maria L. Wei
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
| | - Mikio Tada
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, United States
| | - Alexandra So
- School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Rodrigo Torres
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
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21
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Wu A, Ngo M, Thomas C. Assessment of patient perceptions of artificial intelligence use in dermatology: A cross-sectional survey. Skin Res Technol 2024; 30:e13656. [PMID: 38481072 PMCID: PMC10938028 DOI: 10.1111/srt.13656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/24/2024] [Accepted: 03/01/2024] [Indexed: 03/17/2024]
Affiliation(s)
- Alexander Wu
- Department of DermatologyUniversity of Texas Southwestern Medical CenterDallasUSA
| | - Madeline Ngo
- Department of DermatologyUniversity of Texas Southwestern Medical CenterDallasUSA
| | - Cristina Thomas
- Department of DermatologyUniversity of Texas Southwestern Medical CenterDallasUSA
- Department of Internal MedicineUniversity of Texas Southwestern Medical CenterDallasUSA
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22
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Rozario SY, Sarkar M, Farlie MK, Lazarus MD. Responding to the healthcare workforce shortage: A scoping review exploring anatomical pathologists' professional identities over time. ANATOMICAL SCIENCES EDUCATION 2024; 17:351-365. [PMID: 36748328 DOI: 10.1002/ase.2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 01/16/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Anatomical pathology (AP) is an anatomy-centric medical specialty devoted to tissue-based diagnosis of disease. The field faces a current and predicted workforce shortage, likely increasing diagnostic wait times and delaying patient access to urgent treatment. A lack of AP exposure is proposed to preclude recruitment to the field, as medical students are afforded only a limited understanding of who a pathologist is and what they do (their professional identity/PI and role). Anatomical sciences educators may be well placed to increase student understanding of anatomical pathologists' PI features, but until features of anatomical pathologists' PI are understood, recommendations for anatomy educators are premature. Thus, this scoping review asked: "What are the professional identity features of anatomical pathologists reported in the literature, and how have these changed over time?" A six-stage scoping review was performed. Medline and PubMed, Global Health, and Embase were used to identify relevant studies (n = 74). Team-based framework analysis identified that features of anatomical pathologists' professional identity encompass five overarching themes: professional practice, views about the role, training and education, personal implications, and technology. Technology was identified as an important theme of anatomical pathologists' PI, as it intersected with many other PI feature themes, including diagnosis and collaboration. This review found that pathologists may sometimes perceive professional competition with technology, such as artificial intelligence. These findings suggest unique opportunities for integrating AP-specific PI features into anatomy teaching, which may foster student interest in AP, and potentially increase recruitment into the field.
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Affiliation(s)
- Shemona Y Rozario
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mahbub Sarkar
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Melanie K Farlie
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Department of Physiotherapy, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Michelle D Lazarus
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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23
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du Crest D, Madhumita M, Rossi A, Sadek A, Haykal D, Fernández-Parrado M, Perandones-González H, Smarrito S, Cartier H, Garson S, Ascher B, Nahai F. Skin & Digital - The 2023 conversation. J Eur Acad Dermatol Venereol 2024; 38:e262-e264. [PMID: 37843113 DOI: 10.1111/jdv.19572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 10/03/2023] [Indexed: 10/17/2023]
Affiliation(s)
| | - M Madhumita
- Department of Dermatology, Saveetha Medical College, Chennai, India
| | - A Rossi
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - A Sadek
- Cairo Hospital for Dermatology & Venereology (Al-Haud Al-Marsoud), Cairo, Egypt
- Ministry of Health & Population, Cairo, Egypt
| | - D Haykal
- Centre Laser Palaiseau, Palaiseau, France
| | - M Fernández-Parrado
- Department of Dermatology, Hospital Universitario de Navarra, Pamplona, Spain
| | | | | | - H Cartier
- Centre Médical Saint Jean, Arras, France
| | | | | | - F Nahai
- The Center for Plastic Surgery at MetroDerm, Atlanta, Georgia, USA
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24
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Lazarus MD, Truong M, Douglas P, Selwyn N. Artificial intelligence and clinical anatomical education: Promises and perils. ANATOMICAL SCIENCES EDUCATION 2024; 17:249-262. [PMID: 36030525 DOI: 10.1002/ase.2221] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 07/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Anatomy educators are often at the forefront of adopting innovative and advanced technologies for teaching, such as artificial intelligence (AI). While AI offers potential new opportunities for anatomical education, hard lessons learned from the deployment of AI tools in other domains (e.g., criminal justice, healthcare, and finance) suggest that these opportunities are likely to be tempered by disadvantages for at least some learners and within certain educational contexts. From the perspectives of an anatomy educator, public health researcher, medical ethicist, and an educational technology expert, this article examines five tensions between the promises and the perils of integrating AI into anatomy education. These tensions highlight the ways in which AI is currently ill-suited for incorporating the uncertainties intrinsic to anatomy education in the areas of (1) human variations, (2) healthcare practice, (3) diversity and social justice, (4) student support, and (5) student learning. Practical recommendations for a considered approach to working alongside AI in the contemporary (and future) anatomy education learning environment are provided, including enhanced transparency about how AI is integrated, AI developer diversity, inclusion of uncertainty and anatomical variations within deployed AI, provisions made for educator awareness of AI benefits and limitations, building in curricular "AI-free" time, and engaging AI to extend human capacities. These recommendations serve as a guiding framework for how the clinical anatomy discipline, and anatomy educators, can work alongside AI, and develop a more nuanced and considered approach to the role of AI in healthcare education.
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Affiliation(s)
- Michelle D Lazarus
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mandy Truong
- Monash Nursing and Midwifery, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Menzies School of Health Research, Darwin, Northern Territory, Australia
| | - Peter Douglas
- Monash Bioethics Centre, Faculty of Arts, Monash University, Clayton, Victoria, Australia
| | - Neil Selwyn
- Monash Data Futures Institute, Monash University, Clayton, Victoria, Australia
- Faculty of Education, Monash University, Clayton, Victoria, Australia
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25
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Joly-Chevrier M, Nguyen AXL, Liang L, Lesko-Krleza M, Lefrançois P. The State of Artificial Intelligence in Skin Cancer Publications. J Cutan Med Surg 2024; 28:146-152. [PMID: 38323537 PMCID: PMC11015717 DOI: 10.1177/12034754241229361] [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] [Indexed: 02/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting. OBJECTIVES To analyze the characteristics and trends of AI skin cancer publications from dermatology journals. METHODS AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI. RESULTS A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%). CONCLUSIONS Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.
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Affiliation(s)
| | | | - Laurence Liang
- Faculty of Engineering, McGill University, Montreal, QC, Canada
| | - Michael Lesko-Krleza
- Division of Computer Engineering, Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Philippe Lefrançois
- Division of Dermatology, Department of Medicine, McGill University, Montreal, QC, Canada
- Division of Dermatology, Department of Medicine, Jewish General Hospital, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
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26
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [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: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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27
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Luo J, Yang Z, Xie Y, He Y, Wu M, Fang X, Liao X. Emerging Trends in Teledermatology Research: A Scientometric Analysis from 2002 to 2021. Telemed J E Health 2024; 30:393-403. [PMID: 37449779 DOI: 10.1089/tmj.2023.0101] [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] [Indexed: 07/15/2023] Open
Abstract
Background: With advances in technology, teledermatology (TD) research has increased. However, an updated comprehensive quantitative analysis of TD research, especially one that identifies emerging trends of TD research in the coronavirus disease 2019 (COVID-19) era, is lacking. Objective: To conduct a scientometric analysis of TD research documents between 2002 and 2021 and explore the emerging trends. Methods: CiteSpace was used to perform scientometric analysis and yielded visualized network maps with corresponding metric values. Emerging trends were identified mainly through burst detection of keywords/terms, co-cited reference clustering analysis, and structural variability analysis (SVA). Results: A total of 932 documents, containing 27,958 cited references were identified from 2002 to 2021. Most TD research was published in journals from the "Dermatology" and "Health Care Sciences & Services" categories. American, Australian, and European researchers contributed the most research and formed close collaborations. Keywords/terms with strong burst values to date were "primary care," "historical perspective," "emerging technique," "improve access," "mobile teledermoscopy (TDS)," "access," "skin cancer," "telehealth," "recent finding," "artificial intelligence (AI)," "dermatological care," and "dermatological condition." Co-cited reference clustering analysis showed that the recently active cluster labels included "COVID-19 pandemic," "skin cancer," "deep neural network," and "underserved population." The SVA identified two reviews (Tognetti et al. and Mckoy et al.) that may be highly cited in the future. Conclusion: During and after the COVID-19 era, emerging trends in research on TD (especially mobile TDS) may be related to skin cancer and AI as well as further exploration of primary care in underserved areas.
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Affiliation(s)
- Jianzhao Luo
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ziyu Yang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yao Xie
- Department of Dermatovenerology, West China Hospital, Sichuan University, Chengdu, China
| | - Yang He
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Miaomiao Wu
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiang Fang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyang Liao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
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28
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Siddiqui F, Aslam D, Tanveer K, Soudy M. The Role of Artificial Intelligence and Machine Learning in Autoimmune Disorders. STUDIES IN COMPUTATIONAL INTELLIGENCE 2024:61-75. [DOI: 10.1007/978-981-99-9029-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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29
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Hwang JK, Del Toro NP, Han G, Oh DH, Tejasvi T, Lipner SR. Review of Teledermatology: Lessons Learned from the COVID-19 Pandemic. Am J Clin Dermatol 2024; 25:5-14. [PMID: 38062339 DOI: 10.1007/s40257-023-00826-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2023] [Indexed: 01/23/2024]
Abstract
Utilization of telemedicine for dermatology has greatly expanded since the start of the COVID-19 pandemic, with over 500 new teledermatology studies published since 2020. An updated review on teledermatology is necessary to incorporate new findings and perspectives, and educate dermatologists on effective utilization. We discuss teledermatology in terms of diagnostic accuracy and clinical outcomes, patient and physician satisfaction, considerations for special patient populations, published practice guidelines, cost effectiveness and efficiency, as well as administrative regulations and policies. Our findings emphasize the need for dermatologist education, prioritization of reliable reimbursement systems, and technological innovations to support the continued development of teledermatology in the post-pandemic era.
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Affiliation(s)
- Jonathan K Hwang
- Department of Dermatology, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA
| | - Natalia Pelet Del Toro
- Department of Dermatology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 1991 Marcus Ave, New Hyde Park, NY, 11042, USA
| | - George Han
- Department of Dermatology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 1991 Marcus Ave, New Hyde Park, NY, 11042, USA
| | - Dennis H Oh
- Department of Dermatology, University of California, San Francisco, 4150 Clement Street, San Francisco, CA, 94121, USA
| | - Trilokraj Tejasvi
- Department of Dermatology, University of Michigan Medicine, 1910 Taubman Center, Ann Arbor, MI, 48109, USA
| | - Shari R Lipner
- Department of Dermatology, Weill Cornell Medicine, 1305 York Avenue, New York, NY, 10021, USA.
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Fernandes JRN, Teles AS, Fernandes TRS, Lima LDB, Balhara S, Gupta N, Teixeira S. Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review. J Clin Med 2023; 13:180. [PMID: 38202187 PMCID: PMC10779723 DOI: 10.3390/jcm13010180] [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: 11/02/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
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Affiliation(s)
- Jacks Renan Neves Fernandes
- PhD Program in Biotechnology—Northeast Biotechnology Network, Federal University of Piauí, Teresina 64049-550, Brazil;
| | - Ariel Soares Teles
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
- Federal Institute of Maranhão, Araioses 65570-000, Brazil
| | - Thayaná Ribeiro Silva Fernandes
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Lucas Daniel Batista Lima
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Surjeet Balhara
- Department of Electronics & Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India;
| | - Nishu Gupta
- Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway;
| | - Silmar Teixeira
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
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Leal JFDC, Barroso DH, Trindade NS, de Miranda VL, Gurgel-Gonçalves R. Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence. Biomedicines 2023; 12:12. [PMID: 38275373 PMCID: PMC10813291 DOI: 10.3390/biomedicines12010012] [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: 11/23/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil. We used a set of 2458 pictures (up to 10 of each lesion) obtained from patients treated between 2015 and 2022 in the Leishmaniasis Clinic at the University Hospital of Brasilia. We divided the picture database into training (80%), internal validation (10%), and testing sets (10%), and trained and tested AlexNet to identify pictures of CL lesions. We performed three simulations and trained AlexNet to differentiate CL from 26 other dermatoses (e.g., chromomycosis, ecthyma, venous insufficiency). We obtained an average accuracy of 95.04% (Confidence Interval 95%: 93.81-96.04), indicating an excellent performance of AlexNet in identifying pictures of CL lesions. We conclude that automated CL identification using AlexNet has the potential to assist clinicians in diagnosing skin lesions. These results contribute to the development of a mobile application to assist in the diagnosis of CL in health care services.
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Affiliation(s)
- José Fabrício de Carvalho Leal
- Graduate Program in Tropical Medicine, Center for Tropical Medicine, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil;
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
| | - Daniel Holanda Barroso
- Postgraduate Program in Medical Sciences, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil;
| | - Natália Santos Trindade
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
| | - Vinícius Lima de Miranda
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
| | - Rodrigo Gurgel-Gonçalves
- Graduate Program in Tropical Medicine, Center for Tropical Medicine, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil;
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
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Ricci Lara MA, Rodríguez Kowalczuk MV, Lisa Eliceche M, Ferraresso MG, Luna DR, Benitez SE, Mazzuoccolo LD. A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population. Sci Data 2023; 10:712. [PMID: 37853053 PMCID: PMC10584927 DOI: 10.1038/s41597-023-02630-0] [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: 05/04/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients.
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Affiliation(s)
- María Agustina Ricci Lara
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina.
- Universidad Tecnológica Nacional, Av. Medrano 951, 1179, Ciudad Autónoma de, Buenos Aires, Argentina.
| | - María Victoria Rodríguez Kowalczuk
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Maite Lisa Eliceche
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - María Guillermina Ferraresso
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Daniel Roberto Luna
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
- Instituto de Medicina Traslacional e Ingeniería Biomédica (IMTIB), UE de triple dependencia CONICET- Instituto Universitario del Hospital Italiano (IUHI) - Hospital ITaliano (HIBA), Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Sonia Elizabeth Benitez
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
- Instituto Universitario del Hospital Italiano, Potosí 4265, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Luis Daniel Mazzuoccolo
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
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Omiye JA, Gui H, Daneshjou R, Cai ZR, Muralidharan V. Principles, applications, and future of artificial intelligence in dermatology. Front Med (Lausanne) 2023; 10:1278232. [PMID: 37901399 PMCID: PMC10602645 DOI: 10.3389/fmed.2023.1278232] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023] Open
Abstract
This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.
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Affiliation(s)
| | - Haiwen Gui
- Department of Dermatology, Stanford University, Stanford, CA, United States
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Stanford, CA, United States
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Zhuo Ran Cai
- Department of Dermatology, Stanford University, Stanford, CA, United States
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Abeck F, Kött J, Bertlich M, Wiesenhütter I, Schröder F, Hansen I, Schneider SW, von Büren J. Direct-to-Consumer Teledermatology in Germany: A Retrospective Analysis of 1,999 Teleconsultations Suggests Positive Impact on Patient Care. Telemed J E Health 2023; 29:1484-1491. [PMID: 36862525 DOI: 10.1089/tmj.2022.0472] [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] [Indexed: 03/03/2023] Open
Abstract
Background: There is a high demand for dermatological care in Germany. As use of teledermatology has increased significantly, this study aimed to investigate the impact of teledermatology on patient care. Methods: This retrospective cross-sectional study used data from a direct-to-consumer teledermatology platform using store-and-forward technology available in Germany between July 2021 and April 2022. Additional patient characteristics were collected using a voluntary follow-up questionnaire, 28 days after teleconsultation. Results: Data of 1,999 enrolled patients were evaluated. Patients had a mean age of 36 years, and 61.2% (1,223/1,999) lived in a rural residence. The most common diagnoses included eczema (36.0%, 701/1,946), fungal diseases (15.4%, 299/1,946), and acne (12.5%, 243/1,946). The follow-up questionnaire was answered by 166 patients (8.3%, 166/1,999). In total, 42.8% (71/166) of patients had undergone no previous medical consultation. The most frequent reason for using teledermatology was the waiting time for a dermatology outpatient appointment (62.0%, 103/166). A total of 62.0% (103/166) participants rated the treatment success as good or very good, while 86.1% (143/166) rated the quality of telemedical care as equal or better to that of an outpatient visit. Conclusion: This study showed that patients often use teledermatology because of functional barriers (waiting times). In this cohort, the diagnoses strongly corresponded to reasons for outpatient presentation. Most patients rated the quality of teledermatology service as at least equivalent to that of outpatient physician visits and reported treatment success. Thus, teledermatology can relieve the burden of outpatient care while providing high benefits from the patient's perspective.
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Affiliation(s)
- Finn Abeck
- Department of Dermatology and Venereology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julian Kött
- Department of Dermatology and Venereology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mattis Bertlich
- Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Isabell Wiesenhütter
- Munich University Institute for Psychotherapy Training (MUNIP), Munich, Germany
- Wellster Healthtech Group, Munich, Germany
| | | | - Inga Hansen
- Department of Dermatology and Venereology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan W Schneider
- Department of Dermatology and Venereology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Piraianu AI, Fulga A, Musat CL, Ciobotaru OR, Poalelungi DG, Stamate E, Ciobotaru O, Fulga I. Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine. Diagnostics (Basel) 2023; 13:2992. [PMID: 37761359 PMCID: PMC10529115 DOI: 10.3390/diagnostics13182992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. RESULTS A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. CONCLUSIONS The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.
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Affiliation(s)
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania; (A.-I.P.); (C.L.M.); (O.-R.C.); (D.G.P.); (O.C.); (I.F.)
| | | | | | | | - Elena Stamate
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania; (A.-I.P.); (C.L.M.); (O.-R.C.); (D.G.P.); (O.C.); (I.F.)
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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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Mancha D, Filipe P. Phototherapy in the artificial intelligence era. PHOTODERMATOLOGY, PHOTOIMMUNOLOGY & PHOTOMEDICINE 2023; 39:538-539. [PMID: 37259232 DOI: 10.1111/phpp.12890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/09/2023] [Accepted: 05/23/2023] [Indexed: 06/02/2023]
Affiliation(s)
- D Mancha
- Dermatology Department, Centro Hospitalar Universitário Lisboa Norte EPE, Lisbon, Portugal
| | - P Filipe
- Dermatology Department, Centro Hospitalar Universitário Lisboa Norte EPE, Lisbon, Portugal
- Dermatology University Clinic, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
- Dermatology Research Unit (PFilipe Lab), Instituto de Medicina Molecular João Lobo Antunes, University of Lisbon, Lisbon, Portugal
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Zhao H, Zheng C, Zhang H, Rao M, Li Y, Fang D, Huang J, Zhang W, Yuan G. Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study. Front Endocrinol (Lausanne) 2023; 14:1224191. [PMID: 37635985 PMCID: PMC10453808 DOI: 10.3389/fendo.2023.1224191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Objectives The aim of this study was to improve the diagnostic performance of nuclear medicine physicians using a deep convolutional neural network (DCNN) model and validate the results with two multicenter datasets for thyroid disease by analyzing clinical single-photon emission computed tomography (SPECT) image data. Methods In this multicenter retrospective study, 3194 SPECT thyroid images were collected for model training (n=2067), internal validation (n=514) and external validation (n=613). First, four pretrained DCNN models (AlexNet, ShuffleNetV2, MobileNetV3 and ResNet-34) for were tested multiple medical image classification of thyroid disease types (i.e., Graves' disease, subacute thyroiditis, thyroid tumor and normal thyroid). The best performing model was then subjected to fivefold cross-validation to further assess its performance, and the diagnostic performance of this model was compared with that of junior and senior nuclear medicine physicians. Finally, class-specific attentional regions were visualized with attention heatmaps using gradient-weighted class activation mapping. Results Each of the four pretrained neural networks attained an overall accuracy of more than 0.85 for the classification of SPECT thyroid images. The improved ResNet-34 model performed best, with an accuracy of 0.944. For the internal validation set, the ResNet-34 model showed higher accuracy (p < 0.001) when compared to that of the senior nuclear medicine physician, with an improvement of nearly 10%. Our model achieved an overall accuracy of 0.931 for the external dataset, a significantly higher accuracy than that of the senior physician (0.931 vs. 0.868, p < 0.001). Conclusion The DCNN-based model performed well in terms of diagnosing thyroid scintillation images. The DCNN model showed higher sensitivity and greater specificity in identifying Graves' disease, subacute thyroiditis, and thyroid tumors compared to those of nuclear medicine physicians, illustrating the feasibility of deep learning models to improve the diagnostic efficiency for assisting clinicians.
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Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med 2023; 13:1214. [PMID: 37623465 PMCID: PMC10455458 DOI: 10.3390/jpm13081214] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.
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Affiliation(s)
- Diana Gina Poalelungi
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
| | - Carmina Liana Musat
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Ana Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Marius Neagu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
- ‘Saint John’ Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Alin Ionut Piraianu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Iuliu Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
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Malvehy J, Dreno B, Barba E, Dirshka T, Fumero E, Greis C, Gupta G, Lacarrubba F, Micali G, Moreno D, Pellacani G, Sampietro-Colom L, Stratigos A, Puig S. Smart e-Skin Cancer Care in Europe During and after the Covid-19 Pandemic: a Multidisciplinary Expert Consensus. Dermatol Pract Concept 2023; 13:e2023181. [PMID: 37557116 PMCID: PMC10412091 DOI: 10.5826/dpc.1303a181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 08/11/2023] Open
Abstract
INTRODUCTION Melanoma is the deadliest of all the skin cancers and its incidence is increasing every year in Europe. Patients with melanoma often present late to the specialist and treatment is delayed for many reasons (delay in patient consultation, misdiagnosis by general practitioners, and/or limited access to dermatologists). Beyond this, there are significant inequalities in skin cancer between population groups within the same country and between countries across Europe. The emergence of the COVID-19 pandemic only aggravated these health deficiencies. OBJECTIVES The aim was to create an expert opinion about the challenges in skin cancer management in Europe during the post COVID-19 acute pandemic and to identify and discuss the implementation of new technologies (including e-health and artificial intelligence defined as "Smart Skin Cancer Care") to overcome them. METHODS For this purpose, an ad-hoc questionnaire with items addressing topics of skin cancer care was developed, answered independently and discussed by a multidisciplinary European panel of experts comprising dermatologists, dermato-oncologists, patient advocacy representatives, digital health technology experts, and health technology assessment experts. RESULTS After all panel of experts discussions, a multidisciplinary expert opinion was created. CONCLUSIONS As a conclusion, the access to dermatologists is difficult and will be aggravated in the near future. This fact, together with important differences in Skin Cancer Care in Europe, suggest the need of a new approach to skin health, prevention and disease management paradigm (focused on integration of new technologies) to minimize the impact of skin cancer and to ensure optimal quality and equity.
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Affiliation(s)
- Josep Malvehy
- Dermatology Department. Hospital Clinic of Barcelona, Spain
- University of Barcelona, Barcelona, Spain. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Biomedical Research Networking Centre on Rare Diseases (CIBERER), ISCIII, Barcelona, Spain
| | - Brigitte Dreno
- Department of Dermatolo-Cancerology, CHU Nantes, CIC 1413, CRCINA, University Nantes, Nantes, France
| | - Enric Barba
- Spanish Melanoma Association, Barcelona, Spain
| | - Thomas Dirshka
- Centroderm Clinic, Wuppertal, and Faculty of Health, University Witten-Herdecke, Witten, Germany
| | | | - Christian Greis
- Department Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | - Girish Gupta
- University Department of Dermatology, Edinburgh Royal Infirmary, Lauriston Building, Edinburgh, UK
| | | | | | - David Moreno
- Dermatology Department, University Hospital Virgen Macarena, Seville, Spain
| | - Giovanni Pellacani
- Dermatology Department. Università degli Studi di Roma La Sapienza. Roma, Italy
| | - Laura Sampietro-Colom
- Assessment of Innovations and New Technologies Unit, Hospital Clínic of Barcelona, University of Barcelona, Barcelona, Spain
| | - Alexander Stratigos
- Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodestrian University of Athens, Athens, Greece
| | - Susanna Puig
- Dermatology Department. Hospital Clinic of Barcelona, Spain
- University of Barcelona, Barcelona, Spain. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Biomedical Research Networking Centre on Rare Diseases (CIBERER), ISCIII, Barcelona, Spain
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Pirrera A, Giansanti D. Human-Machine Collaboration in Diagnostics: Exploring the Synergy in Clinical Imaging with Artificial Intelligence. Diagnostics (Basel) 2023; 13:2162. [PMID: 37443555 DOI: 10.3390/diagnostics13132162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Advancements in artificial intelligence (AI), thanks to IT developments during the COVID-19 pandemic, have revolutionized the field of diagnostics, particularly in clinical imaging [...].
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Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel) 2023; 11:1739. [PMID: 37372857 DOI: 10.3390/healthcare11121739] [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: 04/15/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.
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Affiliation(s)
- Huimin Li
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznan, Poland
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
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Grossarth S, Mosley D, Madden C, Ike J, Smith I, Huo Y, Wheless L. Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods. Curr Oncol Rep 2023; 25:635-645. [PMID: 37000340 PMCID: PMC10339689 DOI: 10.1007/s11912-023-01407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/01/2023]
Abstract
PURPOSE OF REVIEW The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma. RECENT FINDINGS Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.
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Affiliation(s)
- Sarah Grossarth
- Quillen College of Medicine, East Tennessee State University, Johnson City, TN, USA
| | | | - Christopher Madden
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- State University of New York Downstate College of Medicine, Brooklyn, NY, USA
| | - Jacqueline Ike
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Meharry Medical College, Nashville, TN, USA
| | - Isabelle Smith
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Yuankai Huo
- Department of Computer Science and Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lee Wheless
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA.
- Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Tennessee Valley Healthcare System VA Medical Center, Nashville, TN, USA.
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Giansanti D. The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105810. [PMID: 37239537 DOI: 10.3390/ijerph20105810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023]
Abstract
Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows.
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Diaz-Ramón JL, Gardeazabal J, Izu RM, Garrote E, Rasero J, Apraiz A, Penas C, Seijo S, Lopez-Saratxaga C, De la Peña PM, Sanchez-Diaz A, Cancho-Galan G, Velasco V, Sevilla A, Fernandez D, Cuenca I, Cortes JM, Alonso S, Asumendi A, Boyano MD. Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients. Cancers (Basel) 2023; 15:cancers15072174. [PMID: 37046835 PMCID: PMC10093614 DOI: 10.3390/cancers15072174] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/17/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.
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Affiliation(s)
- Jose Luis Diaz-Ramón
- Dermatology Service, Cruces University Hospital, 48903 Barakaldo, Spain
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Jesus Gardeazabal
- Dermatology Service, Cruces University Hospital, 48903 Barakaldo, Spain
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
| | - Rosa Maria Izu
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Dermatology Service, Basurto University Hospital, 48013 Bilbao, Spain
| | - Estibaliz Garrote
- TECNALIA, Basque Research and Technology Alliance (BRTA), 20850 Gipuzkoa, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Javier Rasero
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Aintzane Apraiz
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Cristina Penas
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Sandra Seijo
- Ibermática Innovation Institute, 48170 Zamudio, Spain
| | | | | | - Ana Sanchez-Diaz
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Dermatology Service, Basurto University Hospital, 48013 Bilbao, Spain
| | - Goikoane Cancho-Galan
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Pathology Service, Basurto University Hospital, 48013 Bilbao, Spain
| | - Veronica Velasco
- Dermatology Service, Cruces University Hospital, 48903 Barakaldo, Spain
- Pathology Service, Cruces University Hospital, 48903 Barakaldo, Spain
| | - Arrate Sevilla
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | | | - Iciar Cuenca
- Ibermática Innovation Institute, 48170 Zamudio, Spain
| | - Jesus María Cortes
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
- IKERBASQUE, The Basque Foundation for Science, 48009 Bilbao, Spain
| | - Santos Alonso
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - Aintzane Asumendi
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
| | - María Dolores Boyano
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country/EHU, 48940 Leioa, Spain
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Flament F, Jiang R, Houghton J, Cassier M, Amar D, Delaunay C, Balooch G, Bouhadana E, Aarabi P, Passeron T. Objective and automatic grading system of facial signs from smartphones' pictures in South African men: Validation versus dermatologists and characterization of changes with age. Skin Res Technol 2023; 29:e13257. [PMID: 37113093 PMCID: PMC10234158 DOI: 10.1111/srt.13257] [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: 07/25/2022] [Accepted: 12/02/2022] [Indexed: 04/29/2023]
Abstract
OBJECTIVE To evaluate the capacity of the automatic detection system to accurately grade, from selfie pictures, the severity of eight facial signs in South African men. METHODS Selfie pictures (obtained from frontal and back cameras) of 281 South African men differently aged (20-70 years) were obtained and analyzed by an automatic artificial intelligence (AI)-based automatic grading system. Data were compared with the clinical gradings made by experts and dermatologists. RESULTS In all facial signs, both series of gradings were found highly correlated with, however, different coefficients (0.59-0.95), those of marionette lines and cheek pores being of lower values. No differences were observed between data obtained by frontal and back cameras. With age, in most cases, gradings show up to the 50-59 year age-class, linear-like changes. When compared to men of other ancestries, South African men present lower wrinkles/texture, pigmentation, and ptosis/sagging scores till 50-59 years, albeit not much different in the cheek pores sign. The early onset (mean age) of visibility of wrinkles/texture for South African men were (i.e., reaching grade >1) 39 and 45 years for ptosis/sagging. CONCLUSION This study completes and enlarges the previous works conducted on men of other ancestries by showing some South African specificities and slight differences with men of comparable phototypes (Afro American).
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Affiliation(s)
| | - Ruowei Jiang
- ModiFace ‐ A L'Oréal Group CompanyTorontoOntarioCanada
| | - Jeff Houghton
- ModiFace ‐ A L'Oréal Group CompanyTorontoOntarioCanada
| | | | - David Amar
- L'Oréal Research and InnovationClichyFrance
| | | | | | | | - Parham Aarabi
- ModiFace ‐ A L'Oréal Group CompanyTorontoOntarioCanada
| | - Thierry Passeron
- Department of Dermatology, Université Côte d'AzurCHU NiceNiceFrance
- Université Côte d'AzurINSERM, U1065, C3MNiceFrance
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47
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Bhat RM, Madhumita M, Jayaraman J. Dermatophytoses Severity Score - A novel point-of-care scoring tool to assess the severity of dermatophytosis. Mycoses 2023; 66:354-361. [PMID: 36564986 DOI: 10.1111/myc.13560] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/13/2022] [Accepted: 12/19/2022] [Indexed: 12/25/2022]
Abstract
The rising prevalence of dermatophytosis in tropical countries coupled with drug resistance necessitates an objective scoring system to define the severity, monitor therapeutic response and predict prognoses. We attempted to establish and validate a new scoring system - Dermatophytoses Severity Score (DSS), for dermatophytoses affecting non-glabrous skin. A consensus group was convened to develop an objective and reproducible scoring system to describe the extent and severity of dermatophytosis of 200 consecutive patients with dermatophytosis. A second assessment entailed independent DSS scoring of the same patients by dermatologists and residents who were not part of the consensus group. The main outcome measured was index reliability, assessed in two steps, between the observers. A two-step assessment and DSS grading of 200 consecutive patients with clinically diagnosed dermatophytoses showed high reliability (Cronbach's α test and intraclass correlation coefficient). The DSS has demonstrated high reliability, and it could serve as a novel, reproducible and objective scoring tool for dermatophytosis.
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Affiliation(s)
- Ramesh M Bhat
- Department of Dermatology and Venereology, Father Muller Medical College, Mangalore, India
| | - Monisha Madhumita
- Department of Dermatology and Venereology, Saveetha Medical College, Chennai, India
| | - Jyothi Jayaraman
- Department of Dermatology and Venereology, Father Muller Medical College, Mangalore, India
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Wawer Matos PA, Reimer RP, Rokohl AC, Caldeira L, Heindl LM, Große Hokamp N. Artificial Intelligence in Ophthalmology - Status Quo and Future Perspectives. Semin Ophthalmol 2023; 38:226-237. [PMID: 36356300 DOI: 10.1080/08820538.2022.2139625] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
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Affiliation(s)
| | - Robert P Reimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Alexander C Rokohl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Ludwig M Heindl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
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49
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Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Sci Rep 2023; 13:4293. [PMID: 36922556 PMCID: PMC10015524 DOI: 10.1038/s41598-023-31340-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classification. The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was first assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, specificity and diagnostic accuracy of the ML models. The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n = 82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 96%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n = 53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the final diagnosis of the skin lesion. The overall diagnostic accuracy of the model in this study, under real-life conditions, is lower than that of both GPs and dermatologists. This result aligns with the findings of few existing prospective studies conducted under real-life conditions. The outcomes emphasize the significance of involving clinicians in the training of the model and the capability of ML models to assist GPs, particularly in differential diagnosis. Nevertheless, external testing in real-life conditions is crucial for data validation and regulation of these AI diagnostic models before they can be used in primary care.
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50
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Caffery LJ, Janda M, Miller R, Abbott LM, Arnold C, Caccetta T, Guitera P, Shumack S, Fernández-Peñas P, Mar V, Soyer HP. Informing a position statement on the use of artificial intelligence in dermatology in Australia. Australas J Dermatol 2023; 64:e11-e20. [PMID: 36380357 DOI: 10.1111/ajd.13946] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 10/06/2022] [Accepted: 10/17/2022] [Indexed: 11/17/2022]
Abstract
Artificial Intelligence (AI) is the ability for computers to simulate human intelligence. In dermatology, there is substantial interest in using AI to identify skin lesions from images. Due to increasing research and interest in the use of AI, the Australasian College of Dermatologists has developed a position statement to inform its members of appropriate use of AI. This article presents the ACD Position Statement on the use of AI in dermatology, and provides explanatory information that was used to inform the development of this statement.
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Affiliation(s)
- Liam J Caffery
- Centre for Online Health, The University of Queensland, Brisbane, Queensland, Australia.,Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.,The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | - Monika Janda
- Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia
| | - Robert Miller
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia
| | - Lisa M Abbott
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Chris Arnold
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,Australasian Society of Cosmetic Dermatologists, South Yarra, Victoria, Australia.,BioGrid Australia, Parkville, Victoria, Australia
| | - Tony Caccetta
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia
| | - Pascale Guitera
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia
| | - Stephen Shumack
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,The University of Sydney, Sydney, New South Wales, Australia
| | - Pablo Fernández-Peñas
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,The University of Sydney, Sydney, New South Wales, Australia
| | - Victoria Mar
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,Victorian Melanoma Service, Alfred Health, Victoria, Australia.,School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - H Peter Soyer
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
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