Technologies are increasingly independent and play important roles in society. Artificial intelligence (AI) is a branch of science that can improve various environments and processes. The health sector stands out among these contexts, especially ophthalmology and dentistry. Studies evaluating the impact of using these technologies in these contexts are still developing. There are still few studies that assess how AI can impact the decision-making process of health professionals and how it can improve the quality of care provided to these professionals. In this sense, this study aims to evaluate the perception of the impact of AI on the decision-making process of health professionals and the quality of patient care from the perspective of ophthalmologists and dentists. The methodological strategy used was the application of an online questionnaire with eighteen professionals in these areas. Based on the respondents’ opinions, we sought to assess how these decision-making processes are affected by the use of technologies and how they impact the quality of patient care. As a result, it was observed that AI has become essential and a facilitator of the diagnostic processes. However, it presents some challenges related to cost, accessibility, AI x   professional responsibility, and incentive of agreements.

A smart, environmentally friendly superabsorbent polymer was prepared using solvents. It was polymerization on the microwave rays at a medium capacity and for 25 minutes, where the yellow gel was obtained. The polymer was cut and washed with absolute ethanol and methanol and was dried at a temperature of 60°C. Then, the polymer was extracted and milled with ceramic fat until obtaining a very soft powder, and the tests were taken as a formula for a scanning electron microscope and infrared spectrum. The results showed that the absorption value of polymer is at the equivalent acid function, where the absorption capacity was 467.32 grams. At room temperature, the water retention rate was 71%, and at 50°C, it was 52%, and the gel content was 90%. The results showed an improvement in the properties of the gypsum soil in terms of virtual density, porous, and acidic function, reaching 7.3%. The proportion of significant elements (P, N, Ca, K, Na) and moisture content in the soil was 64%, the cumulative tip amount and the consistency of soil granules through wet and dry palm, penetration resistance, electrical conductivity 4 ms, and organic material content were as follows, and the results were very high.

Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.

In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.