The Future of Public Mental Health: Challenges and Opportunities

McGinty, Beth

doi:10.1111/1468-0009.12622

Cited by 6 publications

(2 citation statements)

References 97 publications

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“…For the PH management of smart cities, research methods have important theoretical and practical significance. It can not only improve the efficiency and effectiveness of PH services, but also help smart cities respond more quickly and accurately to potential large-scale PH events ( 5 , 6 ). Implementing the proposed research plan is expected to enhance public health services in smart cities by offering personalized healthcare interventions, predicting outbreaks using data analytics, and optimizing resource allocation.…”

Section: Introductionmentioning

confidence: 99%

The challenges and benefits of public health in smart cities from a 4 M perspective

Yuan,

Du,

Gao

et al. 2024

Front. Public Health

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IntroductionWith the acceleration of urbanization, public health issues have become increasingly prominent in smart city construction, especially in the face of sudden public health crises. A deep research method for public health management based on a 4M perspective (human, machine, materials, methods) is proposed to effectively address these challenges. Methods: The method involves studying the impact of human factors such as population age, gender, and occupation on public health from a human perspective. It incorporates a machine perspective by constructing a public health prediction model using deep neural networks. Additionally, it analyzes resource allocation and process optimization in public health management from the materials and methods perspectives.ResultsThe experiments demonstrate that the public health prediction model based on deep neural networks achieved a prediction accuracy of 98.6% and a recall rate of 97.5% on the test dataset. In terms of resource allocation and process optimization, reasonable adjustments and optimizations increased the coverage of public health services by 20% and decreased the response time to public health events by 30%.DiscussionThis research method has significant benefits for addressing the challenges of public health in smart cities. It can improve the efficiency and effectiveness of public health services, helping smart cities respond more quickly and accurately to potential large-scale public health events in the future. This approach holds important theoretical and practical significance.

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Section: Introductionmentioning

confidence: 99%

The challenges and benefits of public health in smart cities from a 4 M perspective

Yuan,

Du,

Gao

et al. 2024

Front. Public Health

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“…3,4 On one side, mental health problems are on the rise, and on the other side, healthcare related to mental health faces numerous challenges, such as access to mental health-related services, stigma, lack of human resources, financial allocation, digital divide, early diagnosis, and mental health of vulnerable populations. 5,6 The latest technologies, such as artificial intelligence (AI), have improved healthcare delivery significantly in different settings. 7,8 Even though AI technology can be traced back to the mid-20th century, recent decades have witnessed significant growth in its usage in healthcare.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions – A Narrative Review for a Comprehensive Insight

Alhuwaydi

2024

RMHP

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Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.

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Hospital admissions in adolescents with mental disorders in Spain over the last two decades: a mental health crisis?

Soriano,

Ramos,

López-Ibor

et al. 2024

Eur Child Adolesc Psychiatry

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The Future of Public Mental Health: Challenges and Opportunities

Cited by 6 publications

References 97 publications

The challenges and benefits of public health in smart cities from a 4 M perspective

The challenges and benefits of public health in smart cities from a 4 M perspective

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions – A Narrative Review for a Comprehensive Insight

Hospital admissions in adolescents with mental disorders in Spain over the last two decades: a mental health crisis?

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