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Keywords Melanoma and artificial intell...

Melanoma and artificial intelligence

https://read.qxmd.com/read/38323537/the-state-of-artificial-intelligence-in-skin-cancer-publications
#21
JOURNAL ARTICLE
Maxine Joly-Chevrier, Anne Xuan-Lan Nguyen, Laurence Liang, Michael Lesko-Krleza, Philippe Lefrançois
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...
February 7, 2024: Journal of Cutaneous Medicine and Surgery
https://read.qxmd.com/read/38312244/comparison-of-large-language-models-in-management-advice-for-melanoma-google-s-ai-bard-bingai-and-chatgpt
#22
JOURNAL ARTICLE
Xin Mu, Bryan Lim, Ishith Seth, Yi Xie, Jevan Cevik, Foti Sofiadellis, David J Hunter-Smith, Warren M Rozen
Large language models (LLMs) are emerging artificial intelligence (AI) technology refining research and healthcare. Their use in medicine has seen numerous recent applications. One area where LLMs have shown particular promise is in the provision of medical information and guidance to practitioners. This study aims to assess three prominent LLMs-Google's AI BARD, BingAI and ChatGPT-4 in providing management advice for melanoma by comparing their responses to current clinical guidelines and existing literature...
February 2024: Skin Health Dis
https://read.qxmd.com/read/38310497/fusion-of-deep-learning-with-conventional-imaging-processing-does-it-bring-artificial-intelligence-closer-to-the-clinic
#23
JOURNAL ARTICLE
Jason R Hagerty, Anand Nambisan, R Joe Stanley, William V Stoecker
No abstract text is available yet for this article.
February 1, 2024: Journal of Investigative Dermatology
https://read.qxmd.com/read/38297925/unveiling-the-power-of-convolutional-neural-networks-in-melanoma-diagnosis
#24
JOURNAL ARTICLE
Loïc Van Dieren, Jonathan Z Amar, Naomi Geurs, Tom Quisenaerts, Clément Gillet, Benoit Delforge, Lara De Crane D'heysselaer, E F Filip Thiessen, Curtis L Cetrulo, Alexandre G Lellouch
Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpass the accuracy of dermatologists. However, only 23.8% of dermatologists have good or excellent knowledge of the topic. We believe that the lack of knowledge physicians experience regarding artificial intelligence is an obstacle to its clinical implementation...
October 1, 2023: European Journal of Dermatology: EJD
https://read.qxmd.com/read/38247920/optimizing-skin-cancer-survival-prediction-with-ensemble-techniques
#25
JOURNAL ARTICLE
Erum Yousef Abbasi, Zhongliang Deng, Arif Hussain Magsi, Qasim Ali, Kamlesh Kumar, Asma Zubedi
The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan-Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods...
December 31, 2023: Bioengineering
https://read.qxmd.com/read/38244612/can-chatgpt-vision-diagnose-melanoma-an-exploratory-diagnostic-accuracy-study
#26
JOURNAL ARTICLE
Naweed Shifai, Remco van Doorn, Josep Malvehy, Tobias Sangers
No abstract text is available yet for this article.
January 18, 2024: Journal of the American Academy of Dermatology
https://read.qxmd.com/read/38234043/evaluation-of-an-artificial-intelligence-based-decision-support-for-detection-of-cutaneous-melanoma-in-primary-care-a-prospective-real-life-clinical-trial
#27
JOURNAL ARTICLE
Panagiotis Papachristou, My Söderholm, Jon Pallon, Marina Taloyan, Sam Polesie, John Paoli, Chris D Anderson, Magnus Falk
BACKGROUND: Use of artificial intelligence, 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 health care setting by primary care physicians; with or without access to teledermoscopic support from dermatology clinics...
January 17, 2024: British Journal of Dermatology
https://read.qxmd.com/read/38231164/a-narrative-review-opportunities-and-challenges-in-artificial-intelligence-skin-image-analyses-using-total-body-photography
#28
REVIEW
Clare A Primiero, Gisele Gargantini Rezze, Liam J Caffery, Cristina Carrera, Sebastian Podlipnik, Natalia Espinosa, Susana Puig, Monika Janda, H Peter Soyer, Josep Malvehy
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...
January 16, 2024: Journal of Investigative Dermatology
https://read.qxmd.com/read/38225244/dermatologist-like-explainable-ai-enhances-trust-and-confidence-in-diagnosing-melanoma
#29
JOURNAL ARTICLE
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristian Navarrete-Dechent, Sebastian Podlipnik, Emmanouil Chousakos, Iva Crnaric, Jovana Majstorovic, Linda Alhajwan, Tanya Foreman, Sandra Peternel, Sergei Sarap, İrem Özdemir, Raymond L Barnhill, Mar Llamas-Velasco, Gabriela Poch, Sören Korsing, Wiebke Sondermann, Frank Friedrich Gellrich, Markus V Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Matthias Goebeler, Bastian Schilling, Jochen S Utikal, Kamran Ghoreschi, Stefan Fröhling, Eva Krieghoff-Henning, Titus J Brinker
Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi...
January 15, 2024: Nature Communications
https://read.qxmd.com/read/38214959/acceptance-of-medical-artificial-intelligence-in-skin-cancer-screening-choice-based-conjoint-survey
#30
JOURNAL ARTICLE
Inga Jagemann, Ole Wensing, Manuel Stegemann, Gerrit Hirschfeld
BACKGROUND: There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system...
January 12, 2024: JMIR Formative Research
https://read.qxmd.com/read/38211314/a-generalized-model-for-monitor-units-determination-in-ocular-proton-therapy-using-machine-learning-a-proof-of-concept-study
#31
JOURNAL ARTICLE
Emmanuelle Fleury, Joel Herault, Kees Spruijt, Jasper Kouwenberg, Gaëlle Angellier, Petter Hofverberg, Tomasz Horwacik, Tomasz Kajdrowicz, Jean-Philippe Pignol, Mischa S Hoogeman, Petra Trnková
Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pretreatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments...
January 11, 2024: Physics in Medicine and Biology
https://read.qxmd.com/read/38199280/using-multiple-real-world-dermoscopic-photographs-of-one-lesion-improves-melanoma-classification-via-deep-learning
#32
JOURNAL ARTICLE
Achim Hekler, Roman C Maron, Sarah Haggenmüller, Max Schmitt, Christoph Wies, Jochen S Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F Gellrich, Mildred Sergon, Axel Hauschild, Lars E French, Lucie Heinzerling, Justin G Schlager, Kamran Ghoreschi, Max Schlaak, Franz J Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N Kather, Eva Krieghoff-Henning, Titus J Brinker
No abstract text is available yet for this article.
January 8, 2024: Journal of the American Academy of Dermatology
https://read.qxmd.com/read/38183141/refining-mutanome-based-individualised-immunotherapy-of-melanoma-using-artificial-intelligence
#33
REVIEW
Farida Zakariya, Fatma K Salem, Abdulwhhab Abu Alamrain, Vivek Sanker, Zainab G Abdelazeem, Mohamed Hosameldin, Joecelyn Kirani Tan, Rachel Howard, Helen Huang, Wireko Andrew Awuah
Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale...
January 5, 2024: European Journal of Medical Research
https://read.qxmd.com/read/38181888/artificial-intelligence-in-the-detection-of-skin-cancer-state-of-the-art
#34
JOURNAL ARTICLE
Michał Strzelecki, Marcin Kociołek, Maria Strąkowska, Michał Kozłowski, Andrzej Grzybowski, Piotr M Szczypiński
The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, these systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are an effective tool supporting diagnosticians in many medical specialties...
January 3, 2024: Clinics in Dermatology
https://read.qxmd.com/read/38156628/using-artificial-intelligence-as-a-melanoma-screening-tool-in-self-referred-patients
#35
JOURNAL ARTICLE
Madeleine E Crawford, Kiyana Kamali, Rachel A Dorey, Olivia C MacIntyre, Kristyna Cleminson, Michael L MacGillivary, Peter J Green, Richard G Langley, Kerri S Purdy, Ryan C DeCoste, Jennette R Gruchy, Sylvia Pasternak, Amanda Oakley, Peter R Hull
INTRODUCTION: Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. METHODS: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System® ...
December 29, 2023: Journal of Cutaneous Medicine and Surgery
https://read.qxmd.com/read/38156410/machine-learning-ml-techniques-as-effective-methods-for-evaluating-hair-and-skin-assessments-a-systematic-review
#36
REVIEW
Choudhary Sobhan Shakeel, Saad Jawaid Khan
Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Intelligence (AI) techniques in order to evaluate hair and skin assessments. PubMed, Web of Science, IEEE Xplore, and Science Direct were searched in order to retrieve research publications between 1 January 2010 and 31 March 2020 using appropriate keywords such as "hair and skin analysis...
December 29, 2023: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
https://read.qxmd.com/read/38155295/auditing-the-inference-processes-of-medical-image-classifiers-by-leveraging-generative-ai-and-the-expertise-of-physicians
#37
JOURNAL ARTICLE
Alex J DeGrave, Zhuo Ran Cai, Joseph D Janizek, Roxana Daneshjou, Su-In Lee
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers...
December 28, 2023: Nature Biomedical Engineering
https://read.qxmd.com/read/38150449/skinvit-a-transformer-based-method-for-melanoma-and-nonmelanoma-classification
#38
JOURNAL ARTICLE
Somaiya Khan, Ali Khan
Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite challenging task due to presence of high visual similarities across different classes and variabilities within each class. According to the best of our knowledge, this study represents the classification of Melanoma and Nonmelanoma utilising Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) under the Nonmelanoma class for the first time...
2023: PloS One
https://read.qxmd.com/read/38143790/use-of-an-elastic-scattering-spectroscopy-and-artificial-intelligence-device-in-the-assessment-of-lesions-suggestive-of-skin-cancer-a-comparative-effectiveness-study
#39
JOURNAL ARTICLE
Danielle Manolakos, Genevieve Patrick, John K Geisse, Harold Rabinovitz, Kendall Buchanan, Preston Hoang, Eladio Rodriguez-Diaz, Irving J Bigio, Armand B Cognetta
BACKGROUND: Skin cancer is the most common form of cancer worldwide. As artificial intelligence (AI) expands its scope within dermatology, leveraging technology may aid skin cancer detection. OBJECTIVE: To assess the safety and effectiveness of an elastic-scattering spectroscopy (ESS) device in evaluating lesions suggestive of skin cancer. METHODS: This prospective, multicenter clinical validation study was conducted at 4 US investigational sites...
March 2024: JAAD international
https://read.qxmd.com/read/38141179/the-role-of-artificial-intelligence-and-convolutional-neural-networks-in-the-management-of-melanoma-a-clinical-pathological-and-radiological-perspective
#40
JOURNAL ARTICLE
Joshua Yee, Cliff Rosendahl, Lauren G Aoude
Clinical dermatoscopy and pathological slide assessment are essential in the diagnosis and management of patients with cutaneous melanoma. For those presenting with stage IIC disease and beyond, radiological investigations are often considered. The dermatoscopic, whole slide and radiological images used during clinical care are often stored digitally, enabling artificial intelligence (AI) and convolutional neural networks (CNN) to learn, analyse and contribute to the clinical decision-making. To review the literature on the progression, capabilities and limitations of AI and CNN and its use in diagnosis and management of cutaneous melanoma...
December 22, 2023: Melanoma Research
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