Can AI Help With Medical Research?


Artificial intelligence will be the technological superstar of this decade because it will be at the helm of affairs during the revolution of industrial operations. Several sectors have started realizing the endless potential of AI and machine learning, and it is because AI can minimize the margin of error by eliminating human intervention.

Moreover, the reach of the internet is growing day by day, which is solidifying the neural network. Neural Networks are the backbone of deep learning algorithms, and their implications are seamless in various sectors. 

Currently, AI-powered image recognition tools are already revolutionizing some aspects of the healthcare industry. For example, image recognition technology has helped in quick and accurate diagnosis, resulting in proper treatment of the patients. Additionally, image recognition tools also assist in identifying diseases at their earliest onset. Similarly, the medical research sector has also found various applications of AI-powered tools in their industry. 

In this article, we will discuss several AI applications in medical research. 


Diagnostics are a crucial part of the healthcare industry, and it is the part where everything begins. Diagnostics refer to detecting infections and diseases either at the symptomatic or tissue deviant stage. Hence, accurate diagnosis is key to delivering appropriate medical attention. 

Today, approximately one-third of the healthcare companies are exclusively involved in diagnostics. Hence, AI applications here are limitless. As discussed earlier, we are already using image recognition for accurate and speedy diagnosis.

However, tech giants like IBM and Google have already mapped the possible applications of AI and deep learning in healthcare. They have developed elaborate systems that can deliver the best output. 

For example, IBM in 2016 partnered with a diagnostics giant called Quest Diagnostics. For this reason, IBM segregated its AI healthcare operations from its routine ones, and now IBM Watson Health is in charge of every technical aspect related to healthcare. 

IBM aims to deliver the best possible personalized cancer treatment in the partnership mentioned above by efficiently using cognitive computing via neural networks and genomic tumor sequencing. Such systems are taught in some of the best AI courses, which will familiarize you with all the technical aspects related to diagnostics and healthcare.

Similarly, Google’s DeepMind has also entered into a few partnerships with healthcare giants in the UK. The most notable of these partnerships is the one with Moorfields Eye Hospital. In this partnership, Google is trying to improve the diagnosis and treatment of macular degeneration in aging eyes. 

In radiotherapy, Google is training deep learning algorithms to differentiate between healthy tissue and a cancerous one. This algorithm aims to streamline the radiotherapy process and make it more sustainable. 

Personalized Medicine

The rise of AI has always been prearranged, specifically because personal data is readily available now. It is beneficial in establishing a robust neural network, which is the backbone of AI algorithms. Currently, in the case of prescriptions and medicines, physicians are trying to do away with the traditional approach. 

The traditional approach suggested that if you prescribe medicine, you see familiar symptoms. Now, this approach is not streamlined because every patient is different. Hence, the response to a medication becomes unprecedented. It is the part where AI can contribute by introducing the process of personalized medication. 

Here, the deep learning algorithm will retrieve the patient data from the consolidated servers and assess other factors like environment, family medical history, etc. Additionally, AI can also use the neural network to extract data of patients with similar physiology and classify them into one. 

This system will only get better because we now have certified medical devices that regularly record patients’ activities. This regular data can serve as a goldmine for physicians who did not have detailed data on their patients earlier. Physicians can now assess the exact cause behind an ailment based on routine medical data. 

Furthermore, AI can help with the diagnosis and prescribing the appropriate medication. When connected with the AI-powered tools with healthcare organizations, health monitoring devices can also help prevent disease risks by alarming the patients beforehand. 

Drug Discovery 

Pharma industries inject tons of money into the drug discovery program every year. We know that numerous terminal diseases still do not have proper medication, and local epidemics are not new to the world. 

Hence, there is always a necessity to develop new drugs to combat ever-evolving diseases. We must also factor in the possibility that viruses and bacteria are getting resistant to already discovered drugs. Hence, regular improvements are also required. 

The average cost of developing a new drug is approximately over 2 billion USD. Additionally, many drug trials also fail initial testing because of chemical errors and several other external factors, and it only adds to the already enormous production cost. Huge production costs also mean huge retail prices, a nightmare for disadvantaged patients. 

Therefore, this is the appropriate time for implementing AI in drug discovery mechanisms, and it will quicken the discovery process and decrease developmental costs. 

For example, the Universities of Manchester, Aberystwyth, and Cambridge developed an AI known as “Eve,” which is claimed to optimize the drug discovery process. On the same lines, in 2018, “Eve” has helped researchers develop a new compound that can help combat drug-resistant Malaria. 


The year 2020 made us realize the significance of robust statistical models that could predict the outbreak of a disease so diseases such as the current pandemic (Covid-19) could be contained at the earliest opportunity. It is practically impossible for a human mind to track ever-changing environmental conditions and human behavior. 

Thus, there is no one statistical model that could apply universally. Hence, we can regularly use AI and deep learning algorithms to prepare these models. We can use a global database to feed relevant information and train the algorithm to analyze millions of data points. It can help accurately predict a local outbreak, leading to early containment of the disease. 


If you are looking to contribute to medical sciences by designing efficient algorithms, look no further than Great Learning. On this platform, you can find multiple courses like the AI for leaders program designed by industry experts of the University of Texas at Austin, which will help you build AI applications in medical research.

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