Artificial intelligence in dermatology for the clinician

Patel, Shaan; Wang, Jordan V; Motaparthi, Kiran; Lee, Jason B.

doi:10.1016/j.clindermatol.2021.03.012

Cited by 23 publications

(39 citation statements)

References 26 publications

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“…Its use entails developing algorithms to classify, analyze, and predict data. AI is becoming more complex; artificial neural networks, machine learning, and deep learning are being used to uncover complex associations in many fields of medicine [1]. Artificial neural networks (ANN) are designed to simulate signaling in the brain, similar to human neuronal networks that generate thoughts and actions.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

“…Artificial intelligence (AI) is the programming of machines to imitate human thought and perform similar actions [1]. With the ability of computers to process large amounts of data, AI has found increasing use in a wide variety of medical fields, including dermatology [1]. Dermatologists heavily rely on clinical experience over many years and thousands of patient encounters to discern diagnoses.…”

Section: Introductionmentioning

confidence: 99%

“…The question arises of how artificial intelligence can render similar results and encapsulate subjective clinical findings similar to dermatologists. For example, AI has been applied to dermatology in its ability to process 1.41 million training images and then accurately differentiate biopsy-proven images between both 1) keratinocyte carcinomas and seborrheic keratoses and 2) malignant melanomas and nevi when compared to board-certified dermatologists [1,2]. The recent advancements of AI applied to medicine and dermatology open a window of opportunity for patient evaluation and management.…”

Section: Introductionmentioning

confidence: 99%

“…The application of AI in dermatology includes both diagnostic and therapeutic opportunities for home and clinical settings by both patients and providers [1,3]. One such use includes the evaluation of skin aging [3].…”

Section: Introductionmentioning

confidence: 99%

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Use of Artificial Intelligence in Skin Aging

Chung¹,

Jiminez²,

Saleem³

et al. 2023

OBM Geriatr

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Skin aging is a complex process that involves several extrinsic and intrinsic factors and skin health is an indicator of the well-being of an individual. In recent years, there have been numerous developments using computerized systems to aid in finding solutions and treatments to skin aging processes. Tools like artificial intelligence (AI) can aid in finding solutions and treatments for skin aging. AI can also help in monitoring or identifying early signs of skin aging. Within the field of skin aging, several innovations utilize AI to provide better patient care. There is a gap in knowledge within this field concerning current and future directions concerning skin aging and AI. In this review, we aim to highlight current and prospective applications of AI in skin aging and provide insights into future modalities in this field. Models for AI can serve to increase patient participation in skin-care decisions and eventually enhance the patient-provider experience.

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Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Artificial Intelligence in Skin Aging

Chung¹,

Jiminez²,

Saleem³

et al. 2023

OBM Geriatr

View full text Add to dashboard Cite

show abstract

“…Recently, thanks to this potential, research on ML and its implementation in the clinical setting has proliferated in various fields of healthcare (cf. e.g., De Fauw et al, 2018;Esteva et al, 2019;Johnson et al, 2018;Krittanawong et al, 2017;Patel et al, 2021;Salto-Tellez et al, 2018).…”

Section: Machine Learning In Healthcare Contexts: a Short Overviewmentioning

confidence: 99%

Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

Funer

2022

Philos. Technol.

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The initial successes in recent years in harnessing machine learning (ML) technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as the agent responsible for the implementation of diagnosis, therapy, and care is unable to access the generation of findings and recommendations. There is widespread agreement that, generally, a complete traceability is preferable to opaque recommendations; however, there are differences about addressing ML-based systems whose functioning seems to remain opaque to some degree—even if so-called explicable or interpretable systems gain increasing amounts of interest. This essay approaches the epistemic foundations of ML-generated information specifically and medical knowledge generally to advocate differentiations of decision-making situations in clinical contexts regarding their necessary depth of insight into the process of information generation. Empirically accurate or reliable outcomes are sufficient for some decision situations in healthcare, whereas other clinical decisions require extensive insight into ML-generated outcomes because of their inherently normative implications.

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Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications

Shaw,

Ali,

Nie

et al. 2024

Advanced Intelligent Systems

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Microfluidics has evolved into a transformative technology with far‐reaching applications in biomedical research. However, designing and optimizing custom microfluidic systems remains challenging because of their inherent complexities. Integrating artificial intelligence (AI) with microfluidics promises to overcome these barriers by leveraging AI algorithms to automate device design, streamline experimentation, and enhance diagnostic and therapeutic outcomes. Psoriasis is an incurable dermatological condition that is difficult to diagnose and treat owing to its complex pathogenesis. Traditional diagnostic and therapeutic approaches are often ineffective and fail to address individual variabilities in disease progression and treatment responses. However, AI‐coupled microfluidic platforms have the potential to revolutionize psoriasis research and clinical applications with expansive dermatological applications. AI‐driven microfluidic chips with embedded biosensors have the potential to precisely detect biomarkers (BMs), manipulate biological samples, and mimic psoriasis‐like in vivo and in vitro models, thereby allowing real‐time monitoring and optimized therapeutic testing. This review examines the transformative potential of AI and AI‐powered microfluidic platforms for advancing psoriasis research. It examines the design and mechanisms of AI‐coupled microfluidic platforms for cell screening, disease diagnosis, and drug delivery. It highlights recent advances, clinical applications, challenges, future perspectives, and ethical considerations to enhance personalized care and patient outcomes.

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Artificial intelligence in dermatology for the clinician

Cited by 23 publications

References 26 publications

Use of Artificial Intelligence in Skin Aging

Use of Artificial Intelligence in Skin Aging

Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications

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