Journal of Advanced Research in Medical Science & Technology (ISSN: 2394-6539)

AI Tools for Mitigating Mental Health Challenges Arising from Climate Change

2025-10-04T06:11:02+00:00

The threat that climate change poses to human mental health is becoming more widely acknowledged, in addition to its effects on the environment and the economy. Stress, anxiety, depression, post-traumatic stress disorder (PTSD), and cognitive impairments are among the direct and indirect psychological effects of extreme weather events such floods, heat waves, droughts, thunderstorms, extended downpour, and wildfires. A disproportionate number of vulnerable groups are impacted, including children, the elderly, and people with pre-existing medical issues. The research on the relationship between artificial intelligence (AI) interventions, mental health, and climate change is systematically reviewed in this work. 2,947 articles were first obtained from 11 scientific databases, including Scopus, Web of Science, PubMed, IEEE Xplore, ACM Digital Library, and others, using a PRISMA-driven method. Nineteen papers were chosen for further examination after being vetted for language, relevance, duplication, and consistency with the study's goals. The paper emphasizes how machine learning methods and artificial intelligence (AI) technologies can evaluate, forecast, and lessen the negative effects of climate-related events on mental health. The results show that curated technical repositories and multidisciplinary platforms give a greater percentage of pertinent research, even while huge databases offer extensive coverage. In light of climate change, the study highlights the potential of AI to enhance mental health therapies and points out areas that require more investigation.

Comparative Study on Early detection of Congenital Heart Disease (CHD) using Fetal echocardiography

2025-10-08T10:04:31+00:00

This review explores the evolving world of detecting and handling heart conditions in babies
before they're born. It connects traditional ways of predicting a baby's outcome with the
exciting new tools of artificial intelligence (AI), along with new, simple tests that aren't
invasive.
Heart defects are a major health issue across the globe, accounting for nearly a third of all
serious birth defects. Despite this, routine screenings during pregnancy are still falling short,
often failing to catch these problems in non-specialist clinics. When a critical heart issue is
missed, it tragically leads to more newborns getting sick or dying.
For years, doctors have relied on a method called the "Cardiovascular Profile" (CVP) score to
check an unborn baby for heart failure and flag high-risk situations. A CVP score of 7 or lower
is a serious warning sign, strongly connected to a higher risk of death.
Now, AI offers a fresh approach to solve the problem of scans being dependent on a person’s
skill level. It can automatically analyze images and spot difficult patterns with a very high
accuracy rate (over 90%). Beyond that, pairing AI with a simple analysis of a mother's saliva
provides an accurate way to screen for specific, serious heart conditions without invasive
procedures.
However, before making this technology available in public, there are some big hurdles that
should be cleared. We need to test that all the data used is consistent, we have to get a clear
understanding of how the decisions are framed by AI (the "black box" problem), and we must
ensure it's rolled out in public in such a way that's fair and ethical for every patient.

A Review of multimodal based deep learning architectures

2025-10-17T09:56:02+00:00

Multimodal deep learning has emerged as a significant approach in medical imaging. It allows the incorporation of complementary data derived from multiple imaging modalities , such as CT, MRI, and PET. This review looks at recent developments in deep learning structures that combine multiple modalities, like CT with MRI, PET with MRI, and CT with PET, to improve disease diagnosis and prognosis. These fusion methods capture both anatomical and functional details. As a result, models can learn richer feature representations that lead to better accuracy and reliability. Structures such as Convolutional Neural Networks, attention-based networks, generative adversarial networks (GANs), and hybrid fusion frameworks have performed exceptionally well in tasks like tumor segmentation, disease classification, and mutation prediction. Studies show notable improvements in diagnosing complex conditions, including lung cancer, brain tumors, Alzheimer’s disease, and esophageal cancer. Additionally, integrating explainable AI methods increases transparency and clarity in clinical decisions. Overall, this review highlights that multimodal deep learning, using effective fusion of techniques like CT and MRI or PET and MRI, is advancing toward more precise, timely, and personalized medical diagnosis.

A Review on Dehumidification using Desiccants along with Vapor Compression Cycle

2025-10-25T15:33:34+00:00

In a country where scarcity of energy is a common thing and also the cost of energy is considerably on a higher side, this paper may be useful for saving energy. This paper shows how new combinations (systems) can be used for air conditioning/dehumidification purposes. This combination improves air quality along with a considerable saving in cost. Ahead, working of vapor compression air conditioning/ dehumidification system along with desiccant system is explained, both methods (vapor compression system alone and vapor compression along with desiccant) are elaborated for thorough understanding.

A Review of Malaria Transmission Models: Integrating Diabetes Comorbidity and Climate Change

2026-01-17T05:23:36+00:00

Malaria and type 2 diabetes represent two grow- ing global health challenges that are increasingly intersecting in tropical and subtropical regions. Climate change further complicates this scenario by expanding mosquito habitats and worsening metabolic complications in diabetic individuals. Re- search shows that diabetic patients face 46 percent higher malaria infection risk and significantly elevated mortality rates compared to non-diabetic individuals. This review examines the evolution of mathematical models for malaria transmission over the past century, from the foundational Ross model (1911) to contemporary frameworks incorporating climate forcing and host heterogeneity. We trace how successive generations of models have added biological realism through
compartmental structures, age-dependent immunity, vector population dynamics, and environmental factors. Despite substantial progress, current models largely assume uniform host susceptibility and recovery rates, overlooking the profound impact of metabolic disorders on infection dynamics. We identify critical research gaps and emphasise the urgent need for integrated modelling approaches that explicitly account for differential transmission probabilities and recovery rates in diabetic versus non-diabetic populations under climate change scenarios. Such models are essential for protecting vulnerable communities facing the converging threats of infectious and chronic diseases in a warming world.

Assessing the Human Health Impacts of Genetically Modified Crops: A Review of Current Evidence and Controversies

2026-01-17T05:15:16+00:00

Genetically modified (GM) crops are a major development in the biotechnology of agriculture, which has the potential to address issues such as food security and malnutrition but elevates the concern of the general population and scientific circles on the potential health hazards. The objective of this paper is to conduct a synthesis of the current knowledge on the effects of GM crops on human health by examining a set of available research papers and views. The
methodology will entail critical literature analysis and synthesis that will address possible benefits, risks recorded and theorised, and sufficiency of the existing safety assessment procedures. Results indicate a multifaceted image: the potential advantages are a higher degree of nutritional value (e.g., Golden Rice), the creation of pharmaceuticals, a decrease in the amount of pesticide exposure by farmers (e.g., Bt crops), and the increase in food security due to higher farmer income (e.g., Bt cotton in India).

Integrating Sustainable Technology, Artificial Intelligence, and Advanced Communication Networks for Climate Change and Disaster Management

2025-10-25T15:29:31+00:00

Since the start of the 21st century, the issues of climate change and disaster management have become major areas of concern in the world. The main factors responsible for the environmental imbalance that is taking place are rapid industrialization, urbanization without limits, and over-exploitation of natural resources. As a result, the weather is becoming more and more erratic and severe. In addition to becoming more frequent, the power of floods, droughts, cyclones and wildfires is also increasing. There are new problems in the world, and these problems can only be solved by new solutions.

The main factors which could have been the leading role in controlling the situation during the good times are sustainable technologies, smart monitoring systems and AI-driven prediction models. These resources are designed to ‘slow down’ the threats, attract the readiness and make the response easier. The report is about how the use of eco-friendly technologies, prediction AI and communication systems can provide the disaster management with the improvements they require.