Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

The successful treatment of skin cancer and better patient outcomes depend on an early and precise diagnosis.This work offers a multiresolution assessment of the contourlet transform for the diagnosis of skin cancer, utilizing its capacity to catch fine features in images at many scales and orientations. The contourlet transform is applied to dermoscopic images to enhance feature extraction, providing a more comprehensive representation of skin lesions compared to traditional methods. The proposed method involves preprocessing dermoscopic images to improve clarity and reduce noise, followed by the application of the contourlet transform to decompose the images into various frequency bands. These decomposed images are then analyzed to extract relevant textural and structural features, which are subsequently used to train a machine learning classifier. A collection of annotated skin lesion photos is used for performance evaluation, and the outcomes are compared with state-of-the-art methods currently in use. The efficacy of the suggested method is evaluated using metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. The findings show that the contourlet transform-based approach performs better than traditional methods in capturing important characteristics of skin lesions, improving the ability to distinguish benign from malignant lesions and improving diagnostic accuracy. The contourlet transform is a formidable tool for the multiresolution analysis of skin cancer images, according to the study's conclusion, and it has a lot of promise for enhancing dermatology computer-aided diagnosis systems.

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An Overview of Tools and Technologies for Anxiety and Depression Management Using AI

Pavlopoulos,

Rachiotis,

Maglogiannis

2024

Applied Sciences

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This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

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Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Cited by 8 publications

References 100 publications

A Digital Twin System for Oil And Gas Industry: A Use Case on Mooring Lines Integrity Monitoring

A Digital Twin System for Oil And Gas Industry: A Use Case on Mooring Lines Integrity Monitoring

Multiresolution Evaluation of Contourlet Transform for the Diagnosis of Skin Cancer

An Overview of Tools and Technologies for Anxiety and Depression Management Using AI

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