How AI and ML is Set to Transform Healthcare and Agriculture in India

How AI and ML is Set to Transform Healthcare and Agriculture in India

AI and ML in Healthcare: It might be easier to state what part of our modern society artificial intelligence (AI) hasn’t touched to show how important it is to our daily lives, business operations, healthcare system, smart Farming, and society. Intelligence machines are influencing nearly every facet of our lives to help improve efficiencies and augment our human capabilities.

AI is so intertwined in all that we do; it’s hard to imagine living life without it. AI is a central tenet for the disruptive changes of the next stage digital revolution, a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet.

In this blog, we are going to discuss, till now the least talked but with the most potential sectors, i.e Healthcare and Agriculture. Covid-19 or the Coronavirus has shown us how inefficient and ineffective our healthcare systems are when faced against such pandemics.

The agriculture sector is also not a different case, recent floods, climate change, bad irrigation pests, bad soil composition, etc and many more problems still need to be managed. 

The most effective and viable solution to this problem is the use of AI and ML. Let’s discuss how it impacts these sectors.

AI in Agriculture

The food crisis has tormented the world for a long. And as if that was not enough, havoc is being unleashed on agriculture due to climate change and we have a ready case for AI and ML applications to tackle the food emergency. This is being promoted as the following major farming upheaval. By utilizing such cutting edge advancements and by working more astute, a great deal of ground can be secured.

Artificial Intelligence has various applications in agriculture ranging from rural automatons, facial acknowledgment, computerized water system frameworks, and driverless tractors. These applications are done in relationship with an alternate sort of sensors, GPS frameworks, radars, and other cutting edge contraptions dependent on AI.

Considering these broad applications, AI is getting a colossal reaction from investors all around the world. Utilizing AI is an efficient way to conduct or monitor possible defects and nutrient deficiencies in the soil. With the image recognition approach, AI identifies possible defects through images captured by the camera. With the help of Al deep learning applications are developed to analyze flora patterns in agriculture. Such AI-enabled applications are supportive in understanding soil defects, plant pests, and diseases.

AI and ML in Healthcare – Monitoring Crop and Soil Health

Utilizing AI is an efficient way to conduct or monitor possible defects and nutrient deficiencies in the soil. With the image recognition approach, AI identifies possible defects through images captured by the camera.

With the help of Al deep learning applications are developed to analyze flora patterns in agriculture. Such AI-enabled applications are supportive in understanding soil defects, plant pests, and diseases.

Livestock Management

Cows and domesticated animals are significant resources for ranchers. They help in the cultivating cycle as well as give different wellsprings of salary as meat just as dairy. Their wellbeing is of most extreme significance, and the entire crowd can be influenced antagonistically by sicknesses of foot and mouth.

Ranchers currently send infrared sensors and other shrewd screens to distinguish irregularities in the crowd development or temperature perusing to recognize and fix the specific animal and keep the sickness from turning into a fiasco.

Driverless tractors

Technology firms across an array of industries have been developing adaptations of driverless vehicle technology for quite some time, and agriculture is no different.

Automated irrigation systems

As any plant grower knows, traditional irrigation management is an arduous task. This is coupled with a heavy reliance on historical weather conditions to predict the required resources.

Decrease pesticide usage

Farmers can use AI to manage weeds by implementing computer vision, robotics, and machine learning. With the help of the AI, data is gathered to keep a check on the weed which helps the farmers to spray chemicals only where the weeds are. This directly reduced the usage of the chemical spraying an entire field. As a result, AI reduces herbicide usage in the field comparatively the volume of chemicals normally sprayed.

AI in Healthcare AI and ML in Healthcare

AI and ML in Healthcare can have a great impact on the healthcare system of our world. Right from diagnosing a patient correctly and fastly to discovering the next age Drugs, AI and ML is going to come handy. Below are areas that can be widely harnessed.

Pandemic Management

Countries like Taiwan, Japan, and Singapore leveraged the power of AI and ML to curtail the spread of coronavirus. Borrowing from the principles of how communicable diseases spread, AI/ ML helped in predicting the spread of the coronavirus and allowed the government agencies to put in place the required logistics, ensure border controls, and protect their most vulnerable staff members.

Administrative applications

There are also a great many administrative applications of AI  in healthcare. The use of AI is somewhat less potentially revolutionary in this domain as compared to patient care, but it can provide substantial efficiencies. These are needed in healthcare because, for example, the average US nurse spends 25% of work time on regulatory and administrative activities.23 The technology that is most likely to be relevant to this objective is RPA. It can be used for a variety of applications in healthcare, including claims processing, clinical documentation, revenue cycle management, and medical records management.24

Some healthcare organisations have also experimented with chatbots for patient interaction, mental health and wellness, and telehealth. These NLP-based applications may be useful for simple transactions like refilling prescriptions or making appointments. However, in a survey of 500 US users of the top five chatbots used in healthcare, patients expressed concern about revealing confidential information, discussing complex health conditions and poor usability.25

Improve Operational Efficiencies

Hospitals and clinics need to ideally oversee different boundaries, for example, temperature, stickiness, and air guideline to run the offices easily. Man-made intelligence is permitting medical clinics to easily complete office the executives while guaranteeing the physical security of the patients and staff individuals. These advances are likewise empowering prescient upkeep of clinic resources and the following of medicinal services gadgets to guarantee appropriate distribution and convey better consideration results.

Drug discovery with the aid of AI/ML techniques

AI and ML techniques are increasingly being chosen by big names in the pharma industry to solve the hellishly difficult problem of successful drug discovery. Some prominent examples — involving Sanofi, Genentech, Pfizer — are drawn from this article.

Going beyond the conventional long-haul process, AI techniques are increasingly being applied to accelerate the fundamental processes of early-stage candidate selection and mechanism discovery.

For instance, biotechnology company Berg uses its AI platform to analyze immense amounts of biological and outcomes data (lipid, metabolite, enzyme, and protein profiles) from patients to highlight key differences between diseased and healthy cells and identify novel cancer mechanisms.

Conclusion

A wide variety of exciting and future-looking applications of AI/ML techniques and platforms, in the space of agriculture and healthcare, are possible and some are even now in action. the known challenges from data privacy and legal frameworks will continue to be obstacles from the full implementation of these systems.

It can be extremely complex to figure out what kind of data can be viewed and used legally by third-party providers (e.g. the owner of the AI and ML tools, physical devices, or platforms). Consequently, a massive rationalization effort of the legal and policy-making is needed, in parallel to address those challenges.

As technologists and AI/ML practitioners, we should strive for a bright future where the power of AI algorithms benefit billions of common people to improve their basic health and well-being.

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