A Frankenstein's lab for growing'spare' human bodies sounds like something ripped straight from an episode of Black Mirror. But scientists really want to make this gruesome concept a reality. In an article published in the MIT Technology Review, three Stanford University scientists argue that so-called'bodyoids' could'revolutionise' medicine. Bodyoids would be physiologically identical to a normal human but engineered not to have consciousness or experience pain, they write. The researchers argue that modern medical science is being held back by a severe shortage of'ethically sourced human bodies'.

Abstract: Background: Understanding cardiovascular artery disease risk factors, the leading global cause of mortality, is crucial for influencing its etiology, prevalence, and treatment. This study aims to evaluate prognostic markers for coronary artery disease in Mashhad using Naive Bayes, REP Tree, J48, CART, and CHAID algorithms. Methods: Using data from the 2009 MASHAD STUDY, prognostic factors for coronary artery disease were determined with Naive Bayes, REP Tree, J48, CART, CHAID, and Random Forest algorithms using R 3.5.3 and WEKA 3.9.4. Model efficiency was compared by sensitivity, specificity, and accuracy. Cases were patients with coronary artery disease; each had three controls (totally 940). Results: Prognostic factors for coronary artery disease in Mashhad residents varied by algorithm. CHAID identified age, myocardial infarction history, and hypertension. CART included depression score and physical activity. REP added education level and anxiety score. NB included diabetes and family history. J48 highlighted father's heart disease and weight loss. CHAID had the highest accuracy (0.80). Conclusion: Key prognostic factors for coronary artery disease in CART and CHAID models include age, myocardial infarction history, hypertension, depression score, physical activity, and BMI. NB, REP Tree, and J48 identified numerous factors. CHAID had the highest accuracy, sensitivity, and specificity. CART offers simpler interpretation, aiding physician and paramedic model selection based on specific. Keywords: RF, Na\"ive Bayes, REP, J48 algorithms, Coronary Artery Disease (CAD).

Artificial Intelligent transformed industries, like engineering, medicine, finance. Predictive models use supervised learning, a vital Machine learning subset. Crucial for model evaluation, cross-validation includes re-substitution, hold-out, and K-fold. This study focuses on improving the accuracy of ML algorithms across three different datasets. To evaluate Hold-out, Hold-out with iteration, and K-fold Cross-Validation techniques, we created a flexible Python program. By modifying parameters like test size, Random State, and 'k' values, we were able to improve accuracy assessment. The outcomes demonstrate the Hold-out validation method's persistent superiority, particularly with a test size of 10%. With iterations and Random State settings, hold-out with iteration shows little accuracy variance. It suggests that there are variances according to algorithm, with Decision Tree doing best for Framingham and Naive Bayes and K Nearest Neighbors for COVID-19. Different datasets require different optimal K values in K-Fold Cross Validation, highlighting these considerations. This study challenges the universality of K values in K-Fold Cross Validation and suggests a 10% test size and 90% training size for better outcomes. It also emphasizes the contextual impact of dataset features, sample size, feature count, and selected methodologies. Researchers can adapt these codes for their dataset to obtain highest accuracy with specific evaluation.

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks.

The rapid spread of COVID-19 and the emergence of new variants underscore the importance of effective screening measures. Rapid diagnosis and subsequent quarantine of infected individuals can prevent further spread of the virus in society. While PCR tests are the gold standard for COVID-19 diagnosis, they are costly and time-consuming. In contrast, urine test strips are an inexpensive, non-invasive, and rapidly obtainable screening method that can provide important information about a patient's health status. In this study, we collected a new dataset and used the RGB (Red Green Blue) color space of urine test strips parameters to detect the health status of individuals. To improve the accuracy of our model, we converted the RGB space to 10 additional color spaces. After evaluating four different machine learning models, we proposed a new ensemble model based on a multi-layer perceptron neural network. Although the initial results were not strong, we were able to improve the model's screening performance for COVID-19 by removing uncertain regions of the model space. Ultimately, our model achieved a screening accuracy of 80% based on urine parameters. Our results suggest that urine test strips can be a useful tool for COVID-19 screening, particularly in resource-constrained settings where PCR testing may not be feasible. Further research is needed to validate our findings and explore the potential role of urine test strips in COVID-19 diagnosis and management.

My start-up is based upon Artificial intelligence. Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured).

Congenital heart disease (CHD), the most common congenital birth defect, has long been known as one of the main causes of infant death during the first year of life.1 More than one million of the world's approximately 135 million newborns are born each year with CHD.21 Over the last century, cardiac surgery has been an effective approach to tackling CHD; its remarkable advance has decreased the mortality rate of newborns with CHD.10 However, that lower mortality rate is mostly observed in developed countries rather than developing ones. Surgical treatment of CHD requires highly skilled surgeons along with complex infrastructures and equipment. While developed countries have perfected their treatment of CHD for more than 50 years, developing countries are still in the early stages. It is estimated that the number of congenital cardiac surgeons needs to increase by 1,250 times to satisfy only the basic needs of CHD treatment worldwide,16 and most of those surgeons reside in developed countries. As a result, the mortality rate in developing countries is currently at 20%, strikingly higher than the 3% to 7% in developed countries,16 not to mention the fact that mortality rates in developing countries are likely underreported due to the lack of proper diagnosis. Remote surgery has been an active field for decades, enabling experienced surgeons to remotely instruct robots (telerobotics) or guide less-experienced surgeons (surgical telementoring).8

Set in 2013, California based AI-centric healthcare providers, Caption Health has raised a fund of up to 53 million dollars to modify equipment and quicken the medical scanning by their registered nurses without undergoing an elaborative training. It was a revisionist approach by Caption Health CEO, Charles Cadieu to bring alteration in the field of medical science with the help of artificial intelligence. Investors seized this opportunity with the pandemic's onset to envision quick popularization of Caption Health and contributed to equipping better AI-powered Softwares for performing ultrasounds and scans. After receiving the market authorization from the U.S. Food and Drug Administration for cardiac ultrasound software last year, it helped to engage even the non-specialist to conduct the ultrasound where the machine automated reading and interpretation of the search results. It further helped to demonstrate the accuracy of machine learning technologies recently. Robert Ochs, deputy director of the FDA's Office of In Vitro Diagnostics and Radiological Health, commented on it: Cardieu observed the significance of this software as it will be a boon to the COVID patients in this time of crisis by quickly detecting any change in the cardiovascular functions.

Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers. In this paper, first we present a comprehensive review of ROC curve and AUC metric. Next, we propose a modified version of AUC that takes confidence of the model into account and at the same time, incorporates AUC into Binary Cross Entropy (BCE) loss used for training a Convolutional neural Network for classification tasks. We demonstrate this on two datasets: MNIST and prostate MRI. Furthermore, we have published GenuineAI, a new python library, which provides the functions for conventional AUC and the proposed modified AUC along with metrics including sensitivity, specificity, recall, precision, and F1 for each point of the ROC curve.

Artificial Intelligence – The word which creates a path to new-age technologies. The word itself says It is nothing but the intelligence exhibited by machines. Although it is artificial, these machines can do the work and react like humans. The technology we are talking about is not the actual AI – Said by Researchers. The future goal of the specialists is to make general or solid AI with the capacity to perform pretty much every discerning errand.