Every part of the body is dependent on the brain’s functioning, hence it is probably the most vital organ after the heart. A healthy brain helps sustain a healthy body. Through CT scans and AI technology, diseases such as meningitis, degenerative disorders, aneurysms, tumors etc. can be detected early and treated. Moreover, neurosurgery performed through robots is probably the most difficult and intricate form of surgery and proper training of AI to guide such procedures ethically is highly important.
The authors present the AI-MIND protocol where the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. The AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables.
https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2023.1289406/full
The paper proposes transfer learning for multi-class classification of brain tumors.First they investigate the performance of several deep learning (DL) architectures including Visual Geometry Group 16 (VGG16), InceptionV3, VGG19, ResNet50, InceptionResNetV2, and Xception. They then develop a multi-class classification model called IVX16 based on the three best-performing TL models.
http://ieeexplore.ieee.org/abstract/document/10100703
The authors aim to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence. The most suitable cranial entry areas were identified with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points.
https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.863633/full
Public health issues have a profound impact on brain health, and the relationship between the two is shaped by social, economic, environmental, and structural factors. Socioeconomic and environmental factors, chronic stress, trauma, racial and gender inequities make early disease identification and treatment challenging.
This paper discusses articles on the effective study, promotion, and deployment of supportive services addressing brain and mental health particularly in traditionally underserved population groups. Several individual-level factors (e.g., modifiable lifestyle factors, social determinants of health), community-level factors (e.g., group perceptions of mental and brain health), and biopsychosocial contributions can help in understanding Public health issues in risk, prevalence, and receipt of treatment in the context of marginalized groups.
https://www.tandfonline.com/doi/full/10.1080/07317115.2022.2158269
Differences in rates of dementia among diverse populations have garnered recent attention, and it is now accepted that health and socioeconomic Public health issues are stronger determinants than race or cultural identifiers of the differences in dementia prevalence as highlighted in this article.
https://jamanetwork.com/journals/jamaneurology/article-abstract/2729093
Since FDA approval of deep brain stimulation (DBS) for essential tremor over 2 decades ago its usage has increased widely, except in underrepresented populations. The authors performed a systematic search using PubMed and Embase for Public health issues related to DBS care. It was found that female gender, minority race, geographic barriers, low socioeconomic status, and the presence of multiple comorbidities are all linked to the underutilization of DBS.
https://thejns.org/view/journals/j-neurosurg/140/4/article-p1137.xml
Artificial Intelligence (AI) can revolutionize brain health diagnostics, treatment, and research, but if not designed inclusively, it risks exacerbating existing public health issues. To ensure AI empowers brain health for everyone, we need interdisciplinary collaboration: clinicians, AI researchers, social scientists, ethicists, and affected communities must co-create solutions.
AI models are often biased against sensitive classes that could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.
https://journals.sagepub.com/doi/full/10.1177/17456916221134490
The authors discuss challenges in the widespread adoption and integration of AI and ML technologies in brain health. Ethical considerations, data privacy concerns, and regulatory frameworks pose significant hurdles that require careful navigation. Additionally, addressing Public health issues in access to AI-enabled healthcare solutions and ensuring distribution of benefits are essential for maximizing the potential of these technologies in improving brain health outcomes globally.