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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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Chen H, Lin MX, Wang LP, Huang YX, Feng Y, Fang LQ, Wang L, Song HB, Wang LG. Driving role of climatic and socioenvironmental factors on human brucellosis in China: machine-learning-based predictive analyses. Infect Dis Poverty 2023; 12:36. [PMID: 37046326 PMCID: PMC10091610 DOI: 10.1186/s40249-023-01087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/16/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND Brucellosis is a common zoonotic infectious disease in China. This study aimed to investigate the incidence trends of brucellosis in China, construct an optimal prediction model, and analyze the driving role of climatic factors for human brucellosis. METHODS Using brucellosis incidence, and the socioeconomic and climatic data for 2014-2020 in China, we performed spatiotemporal analyses and calculated correlations with brucellosis incidence in China, developed and compared a series of regression and Seasonal Autoregressive Integrated Moving Average X (SARIMAX) models for brucellosis prediction based on socioeconomic and climatic data, and analyzed the relationship between extreme weather conditions and brucellosis incidence using copula models. RESULTS In total, 327,456 brucellosis cases were reported in China in 2014-2020 (monthly average of 3898 cases). The incidence of brucellosis was distinctly seasonal, with a high incidence in spring and summer and an average annual peak in May. The incidence rate was highest in the northern regions' arid and continental climatic zones (1.88 and 0.47 per million people, respectively) and lowest in the tropics (0.003 per million people). The incidence of brucellosis showed opposite trends of decrease and increase in northern and southern China, respectively, with an overall severe epidemic in northern China. Most regression models using socioeconomic and climatic data cannot predict brucellosis incidence. The SARIMAX model was suitable for brucellosis prediction. There were significant negative correlations between the proportion of extreme weather values for both high sunshine and high humidity and the incidence of brucellosis as follows: high sunshine, [Formula: see text] = -0.59 and -0.69 in arid and temperate zones; high humidity, [Formula: see text] = -0.62, -0.64, and -0.65 in arid, temperate, and tropical zones. CONCLUSIONS Significant seasonal and climatic zone differences were observed for brucellosis incidence in China. Sunlight, humidity, and wind speed significantly influenced brucellosis. The SARIMAX model performed better for brucellosis prediction than did the regression model. Notably, high sunshine and humidity values in extreme weather conditions negatively affect brucellosis. Brucellosis should be managed according to the "One Health" concept.
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Affiliation(s)
- Hui Chen
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dong-Da-Jie Street, Fengtai District, Beijing, 100071, China
| | - Meng-Xuan Lin
- Academy of Military Medical Sciences, Academy of Military Science of Chinese People's Liberation Army, 27 Taiping Road, Haidian District, Beijing, 100036, China
| | - Li-Ping Wang
- Chinese Centre for Disease Control and Prevention, No. 155 Changbai Road, Changping District, Beijing, 102206, China
| | - Yin-Xiang Huang
- School of Biological Science and Medical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Yao Feng
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Li-Qun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dong-Da Street, Fengtai District, Beijing, 100071, China
| | - Lei Wang
- Academy of Military Medical Sciences, Academy of Military Science of Chinese People's Liberation Army, 27 Taiping Road, Haidian District, Beijing, 100036, China.
| | - Hong-Bin Song
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dong-Da-Jie Street, Fengtai District, Beijing, 100071, China.
| | - Li-Gui Wang
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dong-Da-Jie Street, Fengtai District, Beijing, 100071, China.
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