An artificial intelligence-driven Chatbot called Chat Generative Pre-trained Transformer, created by Open artificial intelligence, is making waves in many industries. Its foundation in the Generative Pre-trained Transformer language model is where its name originates. The most promising aspect of Chat Generative Pre-trained Transformer is that, compared to other artificial intelligence models, it can provide responses to text input that are almost human-like through the use of deep learning techniques. The public's increasing reliance on artificial intelligence technology is indicated by its quick integration across a range of businesses. Therefore, it is crucial to assess Chat Generative Pre-trained Transformer's possible effects on clinical and translational medicine research in academic settings seriously.

Every academic institution is shaken by the recent development of Chat Generative Pre-trained Transformer, and although we still don't fully grasp all of its potential and hazards, it is worthwhile to offer some first analysis. Academic prospects for Chat Generative Pre-trained Transformer are unparalleled because of its remarkable human-like skills, which outperform the majority of recently released tools. Over the past few months, Chat Generative Pre-trained Transformer has received extraordinary interest from the news and academic community (as of 23/02/2023, there were 650,000,000 results in a Google search for the term). Since it is unlikely that the chatbot was designed with the intention of serving as a stand-in for academic writing, its use in academic writing is a byproduct of clever artificial intelligence. If given the chance, students everywhere would find a way around assessments, thus we are all worried that some students would take advantage of it despite its advantages. Although there isn't now a problem involving assessment integrity in academics, it is impossible to overlook the development of powerful artificial intelligence and tools that could facilitate cheating. Some of us think that the use of Chat Generative Pre-trained Transformer in assessments has some epistemic consequences; yet, these risks do not indicate that we will give up. Although we now know that certain university programs—like management studies and information technology—have a higher risk of academic dishonesty, educators are not new to the practice; rather, they are still learning about Chat Generative Pre-trained Transformer. I don't see any strong arguments in favor of allowing its usage in assessments, even though it is unavoidable in certain academic contexts. Instead than teaching students to "copy and paste," they teach them to "think and write critically." Therefore, the fact that Chat Generative Pre-trained Transformer passed the MBA and medical school exams should raise red flags.

Abstract:

Background:

As a result of the availability of high-speed computing devices, disease screening procedures in modern hospitals have significantly improved over the last few decades. As a result of this invention of deep learning procedures (DP), this work implemented modern diagnostic schemes to achieve accurate and fast results when screening patients for diseases with the aid of medical data.

Method:

This study applied pre-trained DP to detect Diabetic Foot Ulcers (DFU) from the test images. This work consists following stages; (i) Resizing, augmenting, and enhancing images, (ii) deep-features mining with a chosen DP, (iii) features reduction using 50% dropout and serial features-fusion, and (iv) Binary-classification through five-fold cross-validation. Two types of disease detection procedures implemented during the investigation: (a) Conventional deep-features and (b) fused deep-features (FD).

Result:

As a result of this study, the FD obtained with VGG16 and ResNet101 enabled 99.5% accuracy in DFU detection using SoftMax classifier.

Conclusion:

This demonstration confirmed that the proposed scheme is effective in detecting DFU from the chosen database.

Anthracyclines are a crucial component of chemotherapy regimens used to treat a range of malignancies in children and adults. However, cardiac dysfunction and heart failure frequently limit the therapeutic efficacy of anthracyclines. The risk of heart dysfunction increases with increasing doses of anthracycline. Cardiotoxicity is a challenging side effect of DOX, which is cumulative and irreversible; this necessitates assessing the cardiac functions in cancer patients before and after the use of    DOX to avoid permanent cardiac damage. Serial measurements of left ventricle ejection fraction (LVEF) are commonly used for cardiac monitoring during anthracycline treatment. In some studies, cardiotoxicity was defined as LVEF decrease by an absolute 10% and/or below 55%; in others, cardiotoxicity was defined as a decrease below 45%. A serious disadvantage of this test is radioactivity exposure and the low predictability of pre-symptomatic cardiac damage. Blood cardiac biomarkers, such as cardiac troponins and B-type natriuretic peptide (BNP), have been used in heart failure diagnostics. Susceptibility to DOX cardiotoxicity is largely individual, with some patients developing cardiomyopathy at low doses and others tolerating much higher cumulative doses, and this may suggest the presence of genetic predisposition factors. Genetic variations in CBR3 and ABCC1 genes were suggested to contribute to DOX adverse effects. This review highlights the genetic basis for anthracycline-related cardiac dysfunction, focuses on particular genes that have been implicated in innate predisposition to ACT, and assessment of cardiotoxicity.