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Papachristou P, Söderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, Anderson CD, Falk M. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. Br J Dermatol 2024; 191:125-133. [PMID: 38234043 DOI: 10.1093/bjd/ljae021] [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: 11/11/2023] [Revised: 01/12/2024] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics. OBJECTIVES To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. METHODS This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data. RESULTS In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. CONCLUSIONS The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.
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Affiliation(s)
- Panagiotis Papachristou
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - My Söderholm
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Ekholmen Primary Healthcare Centre, Region Östergötland, Linköping, Sweden
| | - Jon Pallon
- Department of Clinical Sciences in Malmö, Family Medicine, Lund University, Malmö, Sweden
- Department of Research and Development, Region Kronoberg, Växjö, Sweden
| | - Marina Taloyan
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - Sam Polesie
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Chris D Anderson
- Department of Biomedical and Clinical Sciences, Division of Dermatology and Venereology, Linköping University, Linköping, Sweden
| | - Magnus Falk
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Region Östergötland, Kärna Primary Healthcare Centre, Linköping, Sweden
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Acezat Oliva J, Alarcón Belmonte I, Paredes Costa EJ, Albiol Perarnau M, Goussens A, Vidal-Alaball J. [Teleconsultation: finding its place in primary care]. Aten Primaria 2024; 56:102927. [PMID: 38608402 PMCID: PMC11019093 DOI: 10.1016/j.aprim.2024.102927] [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: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 04/14/2024] Open
Abstract
Teleconsultation is a remote health consultation using information and communication technologies. There are different modalities and specific practical and communication skills are required. Notwithstanding its prominence in Spain, there is little evidence on teleconsultation. This article explores the applicability, barriers, facilitators and future challenges of teleconsultation. While it has the potential to improve access to healthcare, as well as save time and costs for both patients and healthcare professionals, it faces a number of challenges such as the digital divide and resistance to change. To address new challenges and overcome obstacles, it is crucial to gain the trust of patients and professionals. Improving training in the skills required to optimize their use is also essential. Future research should aim to provide robust evidence regarding safety and cost-effectiveness to ensure successful implementation.
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Affiliation(s)
- Jordi Acezat Oliva
- Grup de Salut Digital CAMFIC, Barcelona, España; Servei d'Atenció Primària Dreta i Muntanya, Gerència Territorial Barcelona ciutat, Institut Català de la Salut, Barcelona, España
| | - Iris Alarcón Belmonte
- Grup de Salut Digital CAMFIC, Barcelona, España; Servei d'Atenció Primària Dreta i Muntanya, Gerència Territorial Barcelona ciutat, Institut Català de la Salut, Barcelona, España
| | - Eugeni Joan Paredes Costa
- Grup de Salut Digital CAMFIC, Barcelona, España; Equip d'Atenció Primària Onze de Setembre, Lleida-7 Direcció d'Atenció Primària Lleida. Institut Català de la Salut, Lleida, España; Facultat de Medicina. Universitat de Lleida, Lleida, España.
| | - Marc Albiol Perarnau
- Grup de Salut Digital CAMFIC, Barcelona, España; Centre d'Atenció Primària Cornellà de Llobregat. Servei d'Atenció Primària Baix Llobregat, Metropolitana Sud. Institut Català de la Salut, Barcelona, España
| | - Alyson Goussens
- Grup de Salut Digital CAMFIC, Barcelona, España; CAP Ernest Lluch. Figueres. Servei d'Atenció Primària Girona Nord. Atenció Primària Girona Institut Català de la Salut, Girona, España
| | - Josep Vidal-Alaball
- Grup de Salut Digital CAMFIC, Barcelona, España; Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Barcelona, España; Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, España; Facultat de Medicina, Universitat de Vic-Universitat Central de Catalunya, Vic, Barcelona, España
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3
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Primiero CA, Rezze GG, Caffery LJ, Carrera C, Podlipnik S, Espinosa N, Puig S, Janda M, Soyer HP, Malvehy J. A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography. J Invest Dermatol 2024; 144:1200-1207. [PMID: 38231164 DOI: 10.1016/j.jid.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/19/2023] [Accepted: 11/09/2023] [Indexed: 01/18/2024]
Abstract
Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.
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Affiliation(s)
- Clare A Primiero
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia
| | - Gisele Gargantini Rezze
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Liam J Caffery
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia; Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia; Centre for Online Health, The University of Queensland, Brisbane, Australia
| | - Cristina Carrera
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medicine Department, University of Barcelona, Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Sebastian Podlipnik
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Natalia Espinosa
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Susana Puig
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medicine Department, University of Barcelona, Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Monika Janda
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - H Peter Soyer
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia; Dermatology Department, Princess Alexandra Hospital, Brisbane, Australia
| | - Josep Malvehy
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medicine Department, University of Barcelona, Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain.
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4
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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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5
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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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6
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Evans W, Meslin EM, Kai J, Qureshi N. Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care. J Pers Med 2024; 14:418. [PMID: 38673045 PMCID: PMC11051552 DOI: 10.3390/jpm14040418] [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: 03/06/2024] [Revised: 03/27/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.
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Affiliation(s)
- William Evans
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Eric M. Meslin
- PHG Foundation, Cambridge University, Cambridge CB1 8RN, UK;
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joe Kai
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Nadeem Qureshi
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
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7
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Brancaccio G, Balato A, Malvehy J, Puig S, Argenziano G, Kittler H. Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check. J Invest Dermatol 2024; 144:492-499. [PMID: 37978982 DOI: 10.1016/j.jid.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/08/2023] [Accepted: 10/01/2023] [Indexed: 11/19/2023]
Abstract
The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.
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Affiliation(s)
| | - Anna Balato
- Dermatology Unit, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | | | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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Venkatesh KP, Raza MM, Nickel G, Wang S, Kvedar JC. Deep learning models across the range of skin disease. NPJ Digit Med 2024; 7:32. [PMID: 38341516 DOI: 10.1038/s41746-024-01033-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
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Vidal-Alaball J, Panadés Zafra R, Escalé-Besa A, Martinez-Millana A. The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities. Aten Primaria 2024; 56:102820. [PMID: 38056048 PMCID: PMC10714322 DOI: 10.1016/j.aprim.2023.102820] [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: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 12/08/2023] Open
Abstract
Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.
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Affiliation(s)
- Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Barcelona, Spain; Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Facultat de Medicina, Universitat de Vic-Universitat Central de Catalunya, Vic, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain
| | - Robert Panadés Zafra
- Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain; Equip d'Atenció Primària d'Anoia Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Jorba i Copons, Barcelona, Spain
| | - Anna Escalé-Besa
- Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain; Equip d'Atenció Primària Navàs-Balsareny, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Navàs, Barcelona, Spain.
| | - Antonio Martinez-Millana
- Grup de Salut Digital CAMFIC, Barcelona, Spain; Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
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10
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Nikolakis G, Vaiopoulos AG, Georgopoulos I, Papakonstantinou E, Gaitanis G, Zouboulis CC. Insights, Advantages, and Barriers of Teledermatology vs. Face-to-Face Dermatology for the Diagnosis and Follow-Up of Non-Melanoma Skin Cancer: A Systematic Review. Cancers (Basel) 2024; 16:578. [PMID: 38339329 PMCID: PMC10854718 DOI: 10.3390/cancers16030578] [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: 10/04/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Teledermatology is employed in the diagnosis and follow-up of skin cancer and its use was intensified during and after the COVID-19 pandemic. At the same time, demographic changes result in an overall increase in non-melanoma skin cancer and skin precancerous lesions. The aim of this study was to elucidate the role of teledermatology in comparison to conventional face-to-face dermatology for such lesions and determine the advantages and limitations of this workflow for patients and physicians. METHODS Research was performed using relevant keywords in MEDLINE and CENTRAL. Relevant articles were chosen following a predetermined standardized extraction form. RESULTS Diagnostic accuracy and interrater/intrarater agreement can be considered comparable-although lower-than in-person consultation. Improvement of particular features such as image quality, medical history availability, and teledermoscopy can further increase accuracy. Further aspects of limitations and advantages (mean time-to-assessment, time-to-treatment, cost-effectiveness) are discussed. CONCLUSIONS Teledermatology has comparable diagnostic accuracy with face-to-face dermatology and can be utilized both for the effective triage of non-melanocytic epithelial tumors and precancerous lesions, as well as the follow-up. Easy access to dermatologic consultation with shorter mean times to diagnostic biopsy and/or treatment coupled with cost-effectiveness could compensate for the lower sensitivity of teledermatology and offer easier access to medical care to the affected populations.
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Affiliation(s)
- Georgios Nikolakis
- Departments of Dermatology, Venereology, Allergology and Immunology, Staedtisches Klinikum Dessau, Brandenburg Medical School Theodor Fontane and Faculty of Health Sciences Brandenburg, 06847 Dessau, Germany;
- Docandu Ltd., London Ν8 0ES, UK;
| | - Aristeidis G. Vaiopoulos
- Second Department of Dermatology and Venereology, “Attikon” University General Hospital, National and Kapodistrian University of Athens, 12462 Athens, Greece;
| | - Ioannis Georgopoulos
- Docandu Ltd., London Ν8 0ES, UK;
- Surgical Department, “Agia Sofia” Children’s Hospital, 11527 Athens, Greece
| | | | - George Gaitanis
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
| | - Christos C. Zouboulis
- Departments of Dermatology, Venereology, Allergology and Immunology, Staedtisches Klinikum Dessau, Brandenburg Medical School Theodor Fontane and Faculty of Health Sciences Brandenburg, 06847 Dessau, Germany;
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11
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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