Introduction

Definition and Importance of Public Health

Public health is the science involved in improving and maintaining the health of people and their communities within a population.¹ This is managed through recommending healthy habits, research on disease and preventing disease spread.

Introduction to AI and Big Data

Artificial intelligence (AI) is a process/technique that aims to create machines that show a similar intelligence to humans. Big data is a concept that refers to huge complex datasets that are used by AI to improve its decision-making abilities.² Big data is analyzed through different methods. One of these methods is big data analytics, where several datasets are combined and analyzed through the use of various technologies. 

The information extracted from these datasets shows patterns or trends that can be found, and this way tangible insights are obtained. This is used in businesses to improve their efficiency and profits and big data analytics can be used in research to analyze huge datasets to obtain relevant findings.

Role of AI in Public Health

Data Collection and Integration

Sources of Big Data in public health 

Big Data in public health is collected from a several sources, each contributing essential information that can be used to monitor, assess, and improve public health strategies. Examples include electronic health records, that include a patient’s medical history digitally. This has information on the patient’s past diseases, treatments, etc. 

This is helpful in understanding the prevalence of specific diseases in an area and how healthcare is used within that area to treat such diseases. They provide an idea of patient health over time and across different healthcare providers. Another source is social media, where platforms like Twitter, Facebook, and Instagram provide real-time information on disease spreads. 

Social media users often explain their symptoms and overall health. This gives an idea of potential outbreaks and public concerns. Another source is wearable devices and mobile health apps. These include fitness trackers, smart watches, and phone apps where people track their physical activity, sleep patterns and other health metrics. 

This information gives insights into the lifestyle of people and how that impacts their health. Advances in genomic sequencing have made it possible to collect and analyze genetic data on a large scale. This data can help identify populations at risk for certain diseases and understand the genetic cause of certain diseases. 

Sensors that monitor air, water quality, and other environmental factors provide data that can be used to assess the impact of environmental conditions on public health. 

Disease Prediction and Prevention

Big data and AI have shown to be extremely useful in predicting outbreaks, such as from the flu or COVID-19. Deiner showed that the spread of epidemics can be detected early by analyzing google and twitter searches related to questions about specific symptoms.³ By analyzing specific google search trends with analytics, they were able to detect the incidence of conjunctivitis. 

This suggests that the same can be done with other diseases, and could potentially warn us about epidemics if they are backed with more data sources such as electronical medical records and medical sales. However, this data has to be carefully analyzed as there can be google search errors and limited online access by minority groups.⁴

Personalized Medicine

Big data can also be applied to precision medicine, where clinical trials select patients according to their specific DNA. This way, knowing their DNA profile gives an idea of what the treatments should target, rather than using general treatments that can be used for everyone. 

This can therefore provide medicine that is specific to the selected patients, making it more likely to be effective in treating the disease that affects the individuals. Big Data analytics can be used in this area to provide more accurate data on the DNA profile of individuals to obtain information on the causes of the diseases. Additional information should be considered in the Big data framework, such as diet, exercise, smoking, alcohol, etc., as all of these factors can affect how well the specific treatment affects each patient.⁵

There are some issues involving big data and genetic information, as there are ethical issues involving privacy and information use.

Enhancing Diagnostics

AI can be used to speed up the process of diagnosing diseases. For example, they could potentially be used to convert results from specific tests done into written reports or verbal explanations directly. This can be done through analyzing medical images, including X-rays, MRIs, CT scans and other types of tests. The patterns that are looked for include lesions, tumours, etc. 

In pathology, AI can speed up the process by analyzing the number of cells found in a tissue sample, for example. This can reduce the chances of doctors making mistakes.

AI has demonstrated to be very accurate in detecting diseases like cancer, often showing more precise and effective results than humans.⁶ This has been shown specifically in the fields of radiology and pathology, where AI models are being used to identify and classify various types of cancer at early stages. 

This is crucial for early diagnosis, which can help reduce the disease when caught early on. A challenge in cancer diagnosis is the different interpretations among doctors. AI solves this issue by conducting an objective analysis of medical images.  

For example, in lung cancer screening using CT scans, AI can help radiologists identify nodules that might indicate cancer, reducing the variability in interpretations and improving the consistency of diagnoses across different healthcare providers. Furthermore, AI systems learn and adapt when exposed to more data. 

This ability allows AI to understand the latest advancements in cancer detection and treatment.

Public Health Surveillance

AI and Big Data can be used to analyze health data more efficiently and accurately. This data is not only obtained from hospitals, clinics and public health reports, as is traditionally done. It also analyzes health data from several other sources including social media, electronic health records, etc. to identify patterns that can indicate a health emergency, such as a pandemic.

Additionally, data from wearable devices and mobile phone applications can also be analyzed. This provides insight into activity and sleep patterns, for example. The fact that AI can conduct these analyses greatly speeds up the process, facilitating public health interventions earlier on than traditionally. 

This helps reduce the impact of spreadable diseases and natural disasters. For instance, during the COVID-19 pandemic, AI was used to track the spread of the virus by analyzing data from several sources, including social media posts and news reports.⁷

This real-time analysis allowed public health authorities establish lockdowns, and allocate resources more effectively. AI can also predict the potential spread of diseases and how they may impact different populations, as well as analyzing the efficacy of the measures used to try to prevent disease outbreaks.

Challenges and Ethical Considerations

Although AI and Big Data are extremely useful tools to speed up and improve public health processes, improving patient safety, there are many challenges and ethical aspects that must be considered.

Health Data is a very sensitive type of information, including medical records, lifestyle and genetic information. The wrongful use of this data could lead to several harmful situations, including impacted employment opportunities and insurance coverage. This also could lead to issues with the use of this information for commercial gain by companies. 

These issues are associated with informed consent and transparency loss. Patients may not understand how their data is being used or shared, which is unethical and can lead to breaches in trust. This could result in patient deciding to not share their health information, impacting how effective these records are. 

There are several strategies that can be used to protect health information and therefore patients. Data must be encrypted so that it cannot be read without the appropriate decryption key. Additionally, it should be anonymized so it cannot be linked to the specific patient.

Future Trends in AI, Big Data, and Public Health

AI and Big Data are changing public health immensely. These technologies will continue to improve the analytics processes used to predict health threats before they occur through increasing the detail and speed with which they analyze multiple sources. 

These sources include environmental factors and epidemiological data. AI and Big Data are also improving personalized health interventions as well as driving research related to drug discovery and development. AI can be used to analyze large data sets to identify potential drugs that can be used in simulated clinical trials and figure out how treatment protocols can be improved. 

Due to the advancement of these technologies, public health workers will have to adapt by improving their data literacy and analytical skills, ensuring they learn how to interpret and use Big Data and AI information. This will allow the workflow between AI and healthcare professionals to be efficient, improving public health and patient safety.

Summary

AI and Big Data have greatly improved public health through improving the processes through which health data is collected and analyzed. Public health focuses on improving the health of populations through disease prevention, healthy lifestyle promotion, and research. AI, which mimics human intelligence, and Big Data, which refers to large, complex datasets, are key tools in advancing these goals.

These technologies collect data from several sources including electronic health records, social media, and wearable devices. This improves disease prediction, personalized medicine, and faster, more accurate diagnostics. Although very beneficial, these technologies also bring some challenges, especially involving data privacy and security, which must be addressed in the future.

Looking ahead, AI and Big Data will continue to shape the future of public health, driving advances in predictive analytics, personalized interventions, and drug discovery. Public Health professionals will need to adapt by developing new skills, ensuring that they can efficiently use AI and Big Data to improve public health.