Artificial Intelligence is the magnum opus of the Technology world in our generation. It promises optimization, more efficiency, cheaper operational cost, and operational precision by reducing human error within processes.
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Leading Business owners across industries are investing in Machine Learning and Artificial Intelligence, as big data influence these fields.
According to PWC, 39% of provider executives claim to invest in AI, ML, and predictive analytics for their businesses.
The impact of this investment can bring about numerous benefits for many industries and it’s hard to argue that there is any industry that will not be impacted.
Here are three industries AI is set to change in 2020.
1. Healthcare Industry
The healthcare industry is one of the largest benefactors of a rise in the applications of Artificial Intelligence. Using AI can be a crucial step in the evolution of the healthcare industry in saving far more lives than ever before.
As data-powered AI becomes more commonplace, accurate, and quicker patient diagnostics will help doctors act swiftly. The ability of an AI-backed algorithm to categorize and analyze data can help reduce the time it takes for diseases to be diagnosed while reducing the risk of misdiagnosing a patient.
This will become even better as the industry adopts electronic health record systems, to further improve their efficiency. Furthermore, medical image analysis, genetic analysis, and patient records can be used by AI tools to suggest potentially unique approaches to treatment.
From Virtual nursing assistance for doctors in real-time to administrative assistance, AI is integrating itself within many departments within the healthcare industry. Moreover, Robot-assisted surgery will make up for a massive $40 billion in potential benefits of the healthcare market by 2026, according to Accenture. By 2026, the total is estimated to be $150 billion.
Top 10 Artificial Intelligence Applications
- Robot-Assisted Surgery – Value: $40B
- Virtual Nursing Assistants – Value: $20B
- Administrative Workflow Assistance – Value: $18B
- Fraud Detection – Value: $17B
- Dosage Error Reduction – Value: $16B
- Connected Machines – Value: $14B
- Clinical Trail – Value: $13B
- Preliminary Diagnosis – Value: $5B
- Automated Image Diagnosis – Value $3B
- Cybersecurity – Value $2B
‘Value’ is the estimated potential annual benefits for each application by 2026. Orthopedic surgery-specific AI Applications.
Robot-assisted surgery is a special area of focus that is still being improved upon. It allows doctors to adopt more precision when making incisions during surgery and saves the overall time of a single operation.
With tools such as the AI-Rad Companion Chest CT4, hospitals can use their existing infrastructure to allow for smooth integration into their daily workflow. The AI-powered solution assists radiologists in optimizing their functions. One such example is the measuring of the diameter of the thoracic aorta or highlighting suspicious lesions in the lung.
This revolution empowers doctors to perform their duties in a better manner and makes the healthcare industry more efficient.
2. Mobile App Development Industry
Artificial Intelligence is reducing the amount of burden the app development process puts on developers. Modern App development ecosystems reduce the coding effort that needs to be put in by utilizing predictive text analysis.
This allows developers to reuse code without having to retype an entire string of code over and over again.
Furthermore, automating tasks and their executions based on pre-defined conditions has also allowed developers to optimize their processes. This includes their workload management, task updates, and so on.
Developers spend a significant amount of time simply updating the tasks they’ve completed, their progress of the current task, and the tasks that are pending. AI does all of this for them within modern development systems. This allows them more time to focus on creative problem-solving and saves them a lot of development time.
Furthermore, Machine learning takes this a step further. Developers can use AI to correct errors to create a self-sustaining database of their work to program AI to analyze methods and suggest references of the best methods applied for future use.
The potential for Artificial Intelligence’s impact on Mobile App development is huge.
As time goes on and more data is accumulated, trial and error allow this software to learn and perfect itself. Thus, giving the best application solutions to developers based on their tasks.
One small example of this is a startup called “Seer-tracking”, which aims to create a safe Low Earth Orbit for space missions. The AI-powered tool they utilize tracks space debris with the help of Machine Learning to predict debris movement in Earth’s orbit.
The software itself is self-learning, which allows for the self-optimization of predictions based on data. Similar use cases can be deduced for application development where self-learning software can reduce development effort and let developers focus on more complex tasks.
Another such example is Uber’s ability to track real-life traffic conditions to update the most suitable route for its users. AI provides convenience backed by data to ensure efficiency.
3. Agriculture
As the world’s population rises with a depleting reserve for natural resources, the pressure to “produce more with less” is rising. The integration of AI brings farmers the opportunity to meet this rising demand for food while maintaining high-quality produce.
According to a survey, the projections show that feeding a world population of 9.1 billion people in 2050 would require raising overall food production by some 70 percent between 2005/07 and 2050. AI-led efforts are pivotal if we are to reach these goals of agricultural production by 2050.
Here are some of the ways that AI is transforming Agriculture in 2020 and beyond.
Pest and Weed Control:
Artificial Intelligence has given the agricultural industry the “See and Spray model” which is a significantly better way to fight pest and weed infestation in crops.
Before the arrival of Artificial Intelligence, farmers would blanket spray their entire crop to ensure it was not damaged.
Today, AI-supported robots can control this infestation by analyzing the right amount of herbicides required to fight weeds and pests in a crop. This is done by a monitoring system that uses computer vision to differentiate between crops and pests by targeting them, instead of spraying the entire crop.
Moreover, harvesting robots are being utilized to increase productivity, which transcends any human ability. Robots that can do the work equivalent of 30 laborers a day have shaken up the industry.
Image processing is a tool that empowers AI to deliver results better than human ability and saves a lot of money in the process. The process of eliminating pests is dependent on image processing that is done to ensure that crops are protected against harm.
Yield Improving Algorithms
In a world heavily dominated by Big Data, companies are making algorithms that inform farmers of the best way to increase yield per crop. These algorithms are based on mathematical models and have helped farmers increase their production levels.
Companies have started to develop agricultural yield-boosting algorithms that can guide a farmer’s hand in the decision-making process for what’s best for a crop. As a result of this, farmers have made significant progress in maximizing crop yield, simply by applying the algorithms built to allow computers to imitate the human cognitive ability of reasoning and deduction based on data.
Environmental Analysis
In 2020, all eyes are set on the arrival of Machine Learning models that provide real-time analytics on soil and crop performance.
These models will be able to not only analyze the impact of changing environmental factors on a crop’s performance. Moreover, they can also track the soil conditions and suggest the appropriate amount of water needed for a crop.
These real-time updates can help reduce soil erosion and water wastage to create a more sustainable way of farming.
This is being achieved through drone systems, software technology, and computer models to ensure live-systems tracking in an agricultural field.
This can help farmers use their resources more strategically and improve their crop yields to match the rising demand for the future.
