Artificial intelligence (AI) is reshaping healthcare, but without safeguards it can entrench disparities and erode trust. We advance an Equity-by-Design governance approach that embeds fairness audits, ethical oversight, and transparency across the AI lifecycle. Drawing on literature and regulation (e.g., EU AI Act, FDA GMLP) and two case studies, we show how continuous auditing, lifecycle checkpoints, institutional review, and post-deployment monitoring surface and mitigate demographic bias. We outline a practical architecture (metrics, dashboards, documentation) aligned with clinical workflows and regulatory duties, and conclude with policy and research actions to operationalize equity, accountability, and trustworthy deployment in diverse healthcare settings.
Air Quality Index (AQI) has been a viral issue since the Coronavirus Disease 2019 (COVID-19) outbreak worldwide. Much research has been published on the topic of COVID-19 and its correlation with the air quality parameters such as Ozone (O3), Nitrogen Dioxide (NO2), Particulate Matter (PM) with microns size of 2.5, 10, and other parameters related to the measurement of the air quality. This paper research proposed and continued the issue of the correlation between the air quality parameters with the pandemic of COVID-19 and compared it to the improvement of the traffic location in Jakarta. We used several air quality parameters to measure the AQI in Jakarta during COVID-19. The air quality parameters are NO2, SO2, CO, O3, and PM10. The results showed a significant improvement in the air quality parameters when compared pre and during COVID-19. Some locations of research that are Kebon Jeruk and Kelapa Gading, have the better air quality since the pandemic COVID-19 meanwhile, other research locations that are Lubang Buaya, Jagakarsa, and Thamrin, did not have a significant impact on the COVID-19.
Heart disease is one of the diseases known to have highest mortality rate, with estimated that one in third of death per year is caused by heart disease. Heart disease comes randomly and can’t be predicted on whether a patient has a heart attack. Potential of heart attack can be predicted with analysing the health situation of the patient. This research is used to predict the heart disease potential in a patient. This research uses statistical methods, namely Logistic Regression and uses deep learning, namely Neural Network. Comparison between the two models is expected to produce the best model for predicting heart disease. In this experiment the neural network has a very high accuracy reaching from 80
Recent advances in neuroimaging, genomics, and other technology driven data acquisition methods have greatly increased the complexity and volume of medical data. Traditional machine learning (ML) approaches are becoming increasingly difficult to apply in this context, particularly in neuroscience, where datasets are often high-dimensional but contain a limited number of samples because of the difficulty of collecting data from human participants. Although ML techniques are powerful tools for analyzing large datasets, they typically require substantial training sets containing balanced data and accurate labels. In real-world medical research, such data is rather rare. Consequently, small sample sizes can introduce bias in model performance estimates, thereby limiting the feasibility of predictive modeling. Nevertheless, such datasets are essential for identifying potential biomarkers and for conducting pilot or feasibility studies within the framework of personalized medicine. However, the limited sample size can lead to biased machine learning performance estimates, which makes it impossible to apply ML methods to predictive modeling. Therefore, artificial intelligence-based data mining tools are being developed to process large volumes of data and explore hidden features and correlations. This narrative review provides an overview of ML strategies tailored to neurological datasets with limited sample sizes, to better understand recent trends in this area and identify opportunities for future research. Particular attention is given to dimensionality reduction in complex data with few instances, as well as the integration of data mining and statistical learning techniques to improve the analysis and interpretation of small-scale but information-rich datasets.
The detection of somatic mutations and Copy number alterations (CNAs) in cancer cells is crucial for diagnosis and careful observation; however, existing traditional methods are both insufficient and inefficient. Examining the latest advances in Machine learning (ML) techniques, such as the Random forest algorithm, and ensemble models such as SomaticSeq, which turns out to have good mutation detection accuracy and efficiency. This method also proves capable of handling a variety of sample purity and sequencing strategies. Therefore, this method can offer good results and efficiency levels when compared to conventional approaches. Although there are still challenges, such as the need for a capable training dataset and high computational requirements, this ML model promises to make significant progress in cancer diagnosis, early detection, and personalized treatment. This review paper aims to review the ML methods that involve the detection of somatic mutations and (CNA). The results of the review showed that the ML method was promising in both implications, as proven by the minimum accuracy being above 70
