Challenges of artificial intelligence in medicine and dermatology

Clinics in Dermatology, Journal Year: 2024, Volume and Issue: 42(3), P. 210 - 215

Published: Jan. 4, 2024

Language: Английский

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The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(3), P. e0273445 - e0273445

Published: March 23, 2023

Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung diagnosis mainly relies on manual pathology section analysis, but the low efficiency subjective nature of film reading can lead to certain misdiagnoses omissions. With continuous development science technology, artificial intelligence (AI) has been gradually applied imaging diagnosis. Although there are reports AI-assisted diagnosis, still problems such as small sample size untimely data updates. Therefore, in this study, large amount recent was included, meta-analysis used evaluate value AI for help STATA16.0, assessed by specificity, sensitivity, negative likelihood ratio, positive diagnostic plotting working characteristic curves subjects. Meta-regression subgroup analysis were investigate The results showed that combined sensitivity AI-aided system 0.87 [95% CI (0.82, 0.90)], specificity 0.91)] (CI stands confidence interval.), missed rate 13%, misdiagnosis ratio 6.5 (4.6, 9.3)], 0.15 (0.11, 0.21)], 43 (24, 76)] sum area under subject operating (SROC) curve 0.93 (0.91, 0.95)]. Based results, CT (Computerized Tomography), considerable accuracy which significant greater feasibility realizing extension application field

Language: Английский

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Artificial intelligence in the detection of skin cancer: State of the art

Clinics in Dermatology, Journal Year: 2024, Volume and Issue: 42(3), P. 280 - 295

Published: Jan. 3, 2024

Language: Английский

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Ethical considerations for artificial intelligence in dermatology: a scoping review

British Journal of Dermatology, Journal Year: 2024, Volume and Issue: 190(6), P. 789 - 797

Published: Feb. 8, 2024

The field of dermatology is experiencing the rapid deployment 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 diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed identify AI and understand their implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library Google Scholar primary literature, following PRISMA Extension Scoping Reviews guidance. Our advanced query included terms related dermatology, considerations. yielded 202 papers. After initial screening, 68 studies were included. Thirty-two clinical image analysis raised misdiagnosis, data security, privacy violations replacement dermatologist jobs. Seventeen discussed limited colour representation in datasets leading potential misdiagnosis general population. Nine articles teledermatology concerns, including exacerbation health disparities, lack standardized regulations, informed consent use challenges. Seven addressed inaccuracies responses models. examined attitudes toward trust AI, with most patients requesting supplemental assessment by physician ensure reliability accountability. Benefits integration into practice include increased patient access, improved decision-making, efficiency many others. However, safeguards must be put place application AI.

Language: Английский

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Artificial intelligence in dermatology: advancements and challenges in skin of color

International Journal of Dermatology, Journal Year: 2024, Volume and Issue: 63(4), P. 455 - 461

Published: March 6, 2024

Abstract Artificial intelligence (AI) uses algorithms and large language models in computers to simulate human‐like problem‐solving decision‐making. AI programs have recently acquired widespread popularity the field of dermatology through application online tools assessment, diagnosis, treatment skin conditions. A literature review was conducted using PubMed Google Scholar analyzing recent (from last 10 years October 2023) evaluate current use for dermatologic purposes, identifying challenges this technology when applied color (SOC), proposing future steps enhance role practice. Challenges surrounding its SOC stem from underrepresentation datasets issues with image quality standardization. With these existing issues, inevitably do worse at lesions SOC. Additionally, only 30% identified had data reported on their dermatology, specifically Significant development applications is required accurate depiction darker tone images datasets. More research warranted better understand efficacy aiding diagnosis options patients.

Language: Английский

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Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Advanced Intelligent Systems, Journal Year: 2024, Volume and Issue: 6(5)

Published: April 21, 2024

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks SRS, such as the limited ability process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating extending human intelligence. AI offers new direction impetus SRS by enhancing performance areas perception, navigation, planning, control strategies. This review introduces developmental history of AI‐aided summarizes basic architecture, analyzes how can improve performance. Classical cases impact evidence clinical settings, associated ethical legal considerations are explored. Finally, challenges discussed, including algorithm development, data science, human–robot coordination, trust building between humans robots.

Language: Английский

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Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms

Diagnostics, Journal Year: 2025, Volume and Issue: 15(1), P. 99 - 99

Published: Jan. 3, 2025

Background/Objectives: Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning transfer models have shown promise in diagnosing cancers through image processing. Integrating attention mechanisms (AMs) deep has further enhanced the accuracy medical classification. While significant progress been made, research is needed improve accuracy. Previous studies not explored integration pre-trained Xception model for binary classification cancer. This study aims investigate impact various on model’s performance detecting benign malignant lesions. Methods: We conducted four experiments HAM10000 dataset. Three integrated self-attention (SL), hard (HD), soft (SF) mechanisms, while fourth used standard without mechanisms. Each mechanism analyzed features from uniquely: examined input relationships, hard-attention selected elements sparsely, soft-attention distributed focus probabilistically. Results: AMs into architecture effectively its performance. The alone was 91.05%. With AMs, increased 94.11% using self-attention, 93.29% attention, 92.97% attention. Moreover, proposed outperformed previous terms recall metrics, which are crucial investigations. Conclusions: These findings suggest that can enhance relation complex imaging tasks, potentially supporting earlier improving treatment outcomes.

Language: Английский

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Harnessing the Power of Artificial Intelligence in Pharmaceuticals: Current Trends and Future Prospects

Intelligent Pharmacy, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Diagnostics, Journal Year: 2023, Volume and Issue: 13(19), P. 3147 - 3147

Published: Oct. 7, 2023

Skin lesions are essential for the early detection and management of a number dermatological disorders. Learning-based methods skin lesion analysis have drawn much attention lately because improvements in computer vision machine learning techniques. A review most-recent classification, segmentation, is presented this survey paper. The significance healthcare difficulties physical inspection discussed state-of-the-art papers targeting classification then covered depth with goal correctly identifying type from dermoscopic, macroscopic, other image formats. contribution limitations various techniques used selected study papers, including deep architectures conventional methods, examined. looks into focused on segmentation that aimed to identify precise borders classify them accordingly. These make it easier conduct subsequent analyses allow measurements quantitative evaluations. paper discusses well-known algorithms, deep-learning-based, graph-based, region-based ones. difficulties, datasets, evaluation metrics particular also discussed. Throughout survey, notable benchmark challenges, relevant highlighted, providing comprehensive overview field. concludes summary major trends, potential future directions detection, aiming inspire further advancements critical domain research.

Language: Английский

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An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments

Cosmetics, Journal Year: 2023, Volume and Issue: 10(1), P. 14 - 14

Published: Jan. 11, 2023

Skin type classification is important because it provides guidance for professionals and consumers to recommend select the most appropriate cosmetic products skin care protocols also in clinical research. Several methods have been proposed classifying typologies such as non-invasive bioengineering tools (examples: Corneometer® Sebumeter®), visual tactile (subjective that evaluate appearance, texture, temperature, abnormalities), artificial intelligence-based instruments rating scales, self-report instruments). Examples of known scales used classify aging are Griffiths Photonumeric Scale, Glogau SCINEXA Scale. The Fitzpatrick Phototype Classification Baumann Type System some classification. Despite diversity degree aging, data on scarce not adequately compiled. Validation larger samples with individuals different ethnicities geographic locations needed promote a more universal use. Visual interesting allow be promptly efficiently examined, without using costly or complex equipment, very useful self-assessment context.

Language: Английский

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