Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes.

Introduction: Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects.

Optimizing Pix2Pix GAN with Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare.

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing prevalent challenges in conventional segmentation methods, such as variable polyp appearances, inconsistent image quality, and limited training data, our model significantly augments the precision and reliability of polyp segmentation. The integration of an attention mechanism enables our model to meticulously focus on the intricate features of polyps, improving segmentation accuracy.

GPTFX: A Novel GPT-3 Based Framework for Mental Health Detection and Explanations.

This paper introduces a novel approach GPTFX, an AI-based mental detection with GPT frameworks. This approach leverages GPT embeddings and the fine-tuning of GPT-3. This approach exhibits superior performance in both classifying mental health disorders and generating explanations with accuracy of around 87% in classification and Rouge-L of around 0.

Quantum-Inspired Heuristic Algorithm for Secure Healthcare Prediction Using Blockchain Technology.

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency.

Prediction analysis and quality assessment of microwell array images.

Microwell arrays are widely used for the analysis of fluorescent-labelled biomaterials. For rapid detection and automated analysis of microwell arrays, the computational image analysis is required. Support Vector Machines (SVM) can be used for this task.

Homocysteine and cerebrovascular accidents.

Hyperhomocysteinaemia is rapidly emerging as an important risk factor for coronary artery disease, possibly because of its propensity to accelerate atherosclerosis. Whether it is also a risk factor for cerebrovascular accidents (CVA) is a matter of debate till now, as there are conflicting results of the various prospective studies. The present study was performed to correlate the levels of plasma homocysteine levels with that of ischaemic and haemorrhagic CVA.