Trends in Molecular Medicine, Год журнала: 2024, Номер unknown
Опубликована: Дек. 1, 2024
Язык: Английский
Frontiers in Medicine, Год журнала: 2024, Номер 11
Опубликована: Март 19, 2024
Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive assistive technology. Together with the collection availability large medical data sets, artificial will become powerful tool can be leveraged by physicians in their diagnoses treatment plans for patients. This comprehensive review focuses on current progress toward AI applications patients, primary care providers, dermatologists, dermatopathologists, explores diverse image molecular processing cancer, highlights AI's potential patient self-screening improving diagnostic accuracy non-dermatologists. We additionally delve into challenges barriers clinical implementation, paths forward implementation areas active research.
Язык: Английский
Процитировано
15
Information Fusion, Год журнала: 2024, Номер unknown, С. 102795 - 102795
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
8
Artificial Intelligence Review, Год журнала: 2025, Номер 58(4)
Опубликована: Янв. 25, 2025
Язык: Английский
Процитировано
1
Medicine Advances, Год журнала: 2025, Номер unknown
Опубликована: Фев. 28, 2025
Язык: Английский
Процитировано
1
Advanced Science, Год журнала: 2024, Номер 11(44)
Опубликована: Окт. 3, 2024
Abstract With the fast‐growing and evolving omics data, demand for streamlined adaptable tools to handle bioinformatics analysis continues grow. In response this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly fully automated multi‐omic analyses based on large language models (LLMs). AutoBA simplifies analytical process by requiring minimal user input while delivering detailed step‐by‐step plans various tasks. AutoBA's unique capacity self‐design processes data variations further underscores its versatility. Compared with online bioinformatic services, offers multiple LLM backends, options both local usage, prioritizing security privacy. comparison ChatGPT open‐source LLMs, code repair (ACR) mechanism in improve stability end‐to‐end Moreover, different from predefined pipeline, has adaptability sync emerging tools. Overall, represents advanced convenient tool, offering robustness conventional analyses.
Язык: Английский
Процитировано
5
Health Data Science, Год журнала: 2025, Номер 5
Опубликована: Янв. 1, 2025
Background: Multimodal large language models (LLMs) have shown potential in various health-related fields. However, many healthcare studies raised concerns about the reliability and biases of LLMs applications. Methods: To explore practical application multimodal skin disease identification, to evaluate sex age biases, we tested performance 2 popular LLMs, ChatGPT-4 LLaVA-1.6, across diverse groups using a subset dermatoscopic dataset containing around 10,000 images 3 diseases (melanoma, melanocytic nevi, benign keratosis-like lesions). Results: In comparison deep learning (VGG16, ResNet50, Model Derm) based on convolutional neural network (CNN), one vision transformer model (Swin-B), found that LLaVA-1.6 demonstrated overall accuracies were 3% 23% higher (and F1-scores 4% 34% higher), respectively, than best performing CNN-based baseline while maintaining 38% 26% lower 19% lower), Swin-B. Meanwhile, is generally unbiased identifying these groups, contrast Swin-B, which biased nevi. Conclusions: This study suggests usefulness fairness dermatological applications, aiding physicians practitioners with diagnostic recommendations patient screening. further verify healthcare, experiments larger more datasets need be performed future.
Язык: Английский
Процитировано
0
Diagnostics, Год журнала: 2025, Номер 15(7), С. 939 - 939
Опубликована: Апрель 7, 2025
Background: Artificial intelligence (AI) developed for skin cancer recognition has been shown to have comparable or superior performance dermatologists. However, it is uncertain if current AI models trained predominantly with lighter Fitzpatrick types can be effectively adapted Asian populations. Objectives: A systematic review was performed summarize the existing use of artificial detection in Methods: Systematic search conducted on PubMed and EMBASE articles published regarding amongst Information study characteristics, model outcomes collected. Conclusions: Current studies show optimistic results utilizing Asia. comparison image abilities might not a true representation diagnostic versus dermatologists real-world setting. To ensure appropriate implementation, maximize potential AI, improve transferability across various genotypes cancers, crucial focus prospective, real-world-based practice, as well expansion diversification databases used training validation.
Язык: Английский
Процитировано
0
Scientific Data, Год журнала: 2025, Номер 12(1)
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0
Analytical Chemistry, Год журнала: 2025, Номер unknown
Опубликована: Фев. 11, 2025
Large language models (LLMs) hold significant promise in the field of medical diagnosis. There are still many challenges direct diagnosis hepatocellular carcinoma (HCC). α-Fetoprotein (AFP) is a commonly used tumor marker for liver cancer. However, relying on AFP can result missed diagnoses HCC. We developed an artificial intelligence (AI) agent centered LLMs, named ChatExosome, which created interactive and convenient system clinical spectroscopic analysis ChatExosome consists two main components: first deep learning Raman fingerprinting exosomes derived from Based patch-based 1D self-attention mechanism downsampling, feature fusion transformer (FFT) was designed to process spectra exosomes. It achieved accuracies 95.8% cell-derived 94.1% 165 samples, respectively. The second component chat based LLM. retrieval-augmented generation (RAG) method utilized enhance knowledge related Overall, LLM serves as core this system, capable identifying users' intentions invoking appropriate plugins data This AI focusing exosome spectroscopy diagnosis, enhancing interpretability classification results, enabling physicians leverage cutting-edge research techniques optimize decision-making processes, it shows great potential intelligent
Язык: Английский
Процитировано
0
Journal of Cosmetic Dermatology, Год журнала: 2025, Номер 24(2)
Опубликована: Фев. 1, 2025
Artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, is expanding healthcare, specifically in the field of cosmetic dermatology. These advanced AI systems are designed to process generate human-like text by analyzing vast amounts data. Through natural processing (NLP), LLMs offer innovative solutions that improve patient care, facilitate clinical workflows, accelerate research efforts. However, while these hold significant potential, it crucial address their limitations, as well ethical considerations regulatory challenges associated with use, ensure responsible implementation [1, 2]. At core, sophisticated computer programs capable understanding generating text. Imagine conversing a knowledgeable assistant who can quickly summarize medical research, suggest treatment options, or answer complex questions. achieve this learning patterns from extensive datasets, including books, articles, scientific journals. With training, they synthesize information provide coherent responses user inquiries [3]. several aspects One key application lies education. By simplifying terminology, make procedures, such dermal fillers, neurotoxins, laser therapies, more accessible patients. This improved communication enables patients informed decisions about care. Additionally, tools have potential bridge knowledge gaps underserved communities, although limited digital proficiency internet access still persist. Another critical personalized planning. analyze histories, procedural risks, desired outcomes evidence-based recommendations. For instance, may combining microneedling platelet-rich plasma therapy optimal skin rejuvenation. While personalization enhance outcomes, requires clinician oversight recommendations match individual needs free bias. also contribute administrative efficiency dermatology practices. Integrating into electronic record (EMR) automate tasks note-taking, scheduling, insurance coding. automation reduces burdens, allowing clinicians devote time direct care [4]. Follow-up monitoring additional areas where show efficiency. interact patients' postprocedure, ensuring adherence recovery protocols, identifying complications, assessing satisfaction levels. capabilities continuity valuable feedback for services. In training simulation, advancing Virtual platforms powered simulate scenarios real-time feedback, enabling trainees refine diagnostic skills safe interactive environment. beneficial mastering emerging techniques Emerging technologies, predictive modeling augmented reality (AR), benefiting integration LLMs. example, Gemini combine image analysis effects resurfacing. capability enhances between patients, setting realistic expectations improving satisfaction. Despite present challenges. major concern risk inaccuracies biases. Training datasets lack diversity lead inequitable unreliable some been misrepresented using LLM technology, Skinive AestheticPro AI, which could mislead users actual capabilities. Ethical concerns arise regarding data privacy. Sharing sensitive necessitates robust encryption compliance regulations HIPAA GDPR. Without strict protections, unintentionally compromise confidentiality. Accessibility another issue. Underserved populations, benefit significantly advancements, often face barriers access, low proficiency, high costs. Developing cost-effective offline-compatible essential disparities equitable access. High subscription costs prevent smaller practices underfunded institutions adopting technologies. Addressing financial barrier preventing healthcare inequities. Human remains vital use should complement, not replace, expertise. Ensuring outputs validated helps maintain accuracy provides trust To effective LLMs, validation essential. Tools DermaGPT DeepSkinAI must undergo rigorous testing evaluate reliability real-world settings. customizing technologies established frameworks, FDA guidelines, standardize approval processes decrease risks. Collaboration among developers, dermatologists, policymakers necessary partnership help develop quality, promote transformative [5]. The multi-directional approach. First, quality providing diverse representative minimize Second, conducting peer-reviewed studies validate credibility. Third, investing affordable accessibility gaps, areas. Finally, educating both limitations usage trust. Large set transform engagement, optimizing driving innovation research. realizing full addressing biases, concerns, collaboration innovation, deliver on promise upholding equity, trust, integrity. We confirm manuscript has read approved all authors, requirements authorship stated earlier document met each author believes represents honest work. authors nothing report. declare no conflicts interest. Data sharing applicable article were generated analysed during current study.
Язык: Английский
Процитировано
0