- 1 How Infectious Diseases are dangerous?
- 2 AI for Infectious Diseases Modelling and Decision-making
- 3 Challenges in Healthcare Area
- 4 How AI can help in Infectious Diseases?
- 5 Innovation in Artificial intelligence (AI)
- 6 Applications of Artificial Intelligence
- 7 How can Big Data Support Public Health?
- 8 Conclusion
This article will cover an overview of AI, AI applications in infectious disease, and the way that AI is used to control infectious diseases.
Artificial intelligence is changing the way we study infectious diseases and how we can fight them by better understanding how they grow and respond to treatment.
Advances in artificial intelligence can help us tackle challenges related to infections that remain largely under-investigated.
Although recent advances in computational methods have brought artificial intelligence and machine learning close to medical practice, its application in the field of infectious diseases is just at the beginning.
Yet, public concern about current challenges, such as antimicrobial resistance and multi-drug resistant organisms and/or allergies caused by certain foods will eventually push policymakers forward to take true advantage of new technologies.
How Infectious Diseases are dangerous?
Infectious disease is a complex process that often involves the interplay of several factors. To date, there are still no “silver bullets” for the surge of epidemics that we face in modern society.
Treatment is limited and difficult. Resistance is ubiquitous and requires an approach where prevention is given priority, as was clearly illustrated during the Ebola outbreak in West Africa.
Although several personalizing treatments exist for cancer, we are still at an early stage for infectious diseases.
This, in part, reflects scientific challenges such as the complexity of bacterial infection and the scarcity of data from dry regions.
Respiratory tuberculosis (TB) currently affects around 10.4 million people in the USA and remains a large contributor to mortality and morbidity in developing counties.
The disease predominantly targets the lungs, but can also manifest in other parts of the body, referred to as extrapulmonary TB.
Perhaps the greatest achievement of humanity rests in the eradication of smallpox, a disease that once killed hundreds of thousands per year.
Check out the complete research on AI for Infectious Diseases Here
AI for Infectious Diseases Modelling and Decision-making
Similar attention has been drawn to the field of infectious diseases in recent years, with a growing number of research groups now focusing on the use of machine-learning techniques to help tackle epidemics.
The question of whether artificial intelligence is as good at making clinical decisions as doctors is one that remains unanswered, but the potential for using tools like artificial intelligence is certainly there.
The nature of infectious diseases (ID) is changing, and with this change, we need to change the way that we predict and treat these diseases.
Artificial intelligence (AI) and its components are starting to be leveraged to improve our capability in infectious diseases environments.
We are arguing that extreme values can provide a more predictive signal than the ones derived from existing methods.
Challenges in Healthcare Area
In modern times, where the world is becoming smaller by the second with the advent of industrialization, transportation, and medicine, infectious disease is a global problem that requires a global solution.
Modernized countries are more advanced in terms of epidemiology and have been able to establish preventive measures; however, it is not so for low-income countries.
This topic brings its challenges in terms of treatment and vaccines due to funding constraints from their governments.
According to WHO, an infectious disease is the most common cause of death worldwide and The control of infectious diseases is one of the pillars of public health.
However, traditional approaches based on mathematical models and expert knowledge are currently subject to some limitations.
How AI can help in Infectious Diseases?
Cluster Detection Algorithm use for detecting clusters appeared firstly in graphs used by AI for detecting clusters in data with special structures: biological sequences or high dimensional data sets.
In the past ten years, there has been a rapid increase in mathematical tools that can be used to assist in the field of infectious diseases.
It helps mathematicians predict the likelihood of a disease epidemic by studying viral or bacterial strains of a certain population.
Artificial intelligence and machine learning are two modalities that have influenced this trend, which is likely to gain more traction in the future.
There are many applications of artificial intelligence that can be put into practice today, including risk assessment models and strategies, better drug development pathways and cures, increased chances of survival, and detection of new emerging strains.
Artificial intelligence (AI) is fast becoming a key tool in the fight against infectious diseases.
It will soon identify every pathogen and unravel its complexity, as well as predict epidemics better than ever before.
This innovative technology, coupled with big data, will help us to understand how diseases are transmitted and develop effective treatments.
Innovation in Artificial intelligence (AI)
Artificial intelligence (AI) is one of the most popular topics these days, so it comes as no surprise to see an increasing number of startups in the machine learning field.
IBM has presented its first cognitive system, Watson. Google and Apple have launched their own AI-powered assistants.
NVIDIA has recently announced the acquisition of a French company that creates deep learning systems for medical imaging.
Facebook is continuing to invest heavily in its ML platform, with the announcement of a new tool to help make video recommendations.
Google released a new API on the App Engine console to allow developers to create services that can be used or purchased by a third party.
The Cloud Healthcare API is the first result of Google’s AI initiative which seeks to explore and enhance artificial intelligence in healthcare.
Applications of Artificial Intelligence
1. Computational models are becoming increasingly important in the field of infectious disease epidemic and prevention.
2. New methods for diagnosis using data-driven approaches for better patient care
3. Implementation of artificial intelligence tools to mitigate the spread of nosocomial infections in hospitals
4. Big Data, Cloud computing, and AI: useful tools against epidemics in the age of globalization
5. Artificial Intelligence and Antibiotics Resistance
6. Artificial intelligence could be a powerful tool for epidemiological surveillance
7. AI can Aid clinical decision making during an epidemic event
How can Big Data Support Public Health?
Big data should not replace classical ways of collecting information about an epidemic; it should rather be used as an added aid.
Man is and will always be susceptible to infectious disease. Physicians, researchers, and public health institutions need better tools to better understand, predict and prevent the spread of such malicious pathogens.
As long as infections are not eradicated, artificial intelligence will continue to positively contribute to the field of human health
By integrating big data with current disease surveillance systems, information and medical tools can improve considerably to better understand outbreak risks and optimize possible treatments from past experience. For example, following an outbreak
This article discusses the use of different tools as part of AI, which has the capacity to offer significant benefits.
If the data is properly collected and processed, it provides insights into human behavior, demographics, and traditional norms of affected communities.
This informatics can be harnessed to alter decisions, both on research and policy-making levels, much in the same way computational automation has aided other industries to achieve efficiency, accuracy, and consistency.
Nitin is a professional data Engineer, Who has a Post Graduation in Data Science and Analytics and working in the healthcare sector. Experts in Data analysis, Machine learning, AI, blockchain, Data related tools, and technologies. He is the Co-founder and editor of analyticslearn.com