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7 AI Applications in Life Sciences

AI in Life Sciences

The constant stream of scientific advancements is pushing the boundaries of a standard. Life sciences is not an exception, and by examining the enormous amount of data produced through research and development, we reconsider our understanding of this field. 

Thanks to Artificial intelligence, we can now navigate this vast sea of data and find impressive results.

In this article, we will disclose the benefits of using artificial intelligence in life sciences and provide seven use cases of how it can be applied in this sector.

Benefits of AI in Life Sciences

Benefits of AI in Life Sciences

We’re witnessing AI transforming various sectors. However, in life sciences, its impact is literally vital. 

Some of the benefits of AI in life sciences include:

Acceleration of data processing 

In drug discovery, AI algorithms can analyze large amounts of data to predict drug-target interactions and identify potential drug candidates. This can reduce the time and cost of bringing a new drug to market.

Maximizing the efficiency of treatment

AI can help develop personalized treatment plans for patients by analyzing their genomic data and medical history, leading to improved patient outcomes.

Improving data accuracy

AI can analyze medical images such as MRI, CT scans, and X-rays to identify abnormalities and assist in diagnosis by reducing human error and speeding up diagnosis.

Process optimization

AI tools are capable of optimizing medical processes and clinical trials by identifying the right patient populations, predicting outcomes, and monitoring patient safety. This can lead to faster and more efficient clinical trials, accelerating the development of new treatments.

Improving decision-making

AI can analyze data from clinical trials and real-world sources to identify potential drug safety issues, which can help regulatory authorities make informed decisions about drug approval and withdrawal.

Though AI has the potential to revolutionize the field of life sciences, it comes with some challenges. Below we will list the most important ones.

Challenges of AI implementation in Life Sciences

Lack of talent

The AI industry is facing a shortage of skilled workers, resulting in a need for more qualified individuals. As the pharmaceutical industry has yet to pioneer digital innovation, it is crucial to upskill current industry employees and modify hiring policies to attract skilled data scientists to life sciences positions.

Low-quality data 

The accuracy of AI-generated results could be improved by better data quality. The “garbage in, garbage out” principle is critical in algorithm development, and even experienced tech companies may make errors. When it comes to human well-being or lives, AI needs to provide accurate, impartial responses while making decisions about people’s health.

Lack of data standardization

The absence of industry-wide data standards makes obtaining patient data scattered across various sources difficult. Incorporating data into corporate systems and making it useful requires substantial time and resources, and the development of standardized data formats triggers greater collaboration between pharmaceutical, and biotech companies, data, and technology firms.

7 applications of AI in life sciences

The application of AI in life sciences has already led to groundbreaking innovations. Let’s explore seven exciting applications of AI in life sciences that are changing how professionals now approach research, patient treatment, and many other fields.

1. Provision of personal treatment through AI tools

The application of artificial intelligence helps to eliminate a one-fits-all approach in life science. An example of that is the medication’s dosing and treatment plans. 

By performing a deeper analysis of patients’ data and tracking various parameters, AI allows medical professionals to re-evaluate their treatment plans and find a better alternative for the patients.

Some AI tools specialize in developing deep-learning medical tools that analyze unstructured records (medical history, images, blood tests, EKGs, genome reports) to help doctors cater to real-time patient needs.

2. Acceleration of drug discovery 

Drug discovery is a tedious, time-consuming activity that implies screening through thousands of potential molecules. AI can screen and cross-reference a giant amount of data faster and more efficiently than humans can ever do. This helps to elaborate on a more accurate list of potential drug candidates in a much shorter time frame.

3. Streamlined drug development

New drug manufacturing can take many years and substantial investments. Artificial Intelligence tools help put data from multiple sources, such as hospitals and research labs, into a standardized format. AI can also help develop better healthcare networks and protocols, speeding up their introduction in the market at a reasonable price.

4. AI in clinical trial design

AI is being increasingly utilized in designing clinical trials, estimating ideal sample sizes, and implementing them remotely on participants across wider geographical regions. This reduces costs and enhances the likelihood of obtaining accurate and relevant data.

5. AI for disease diagnosis

Only complete medical records and a high volume of cases can lead to correct disease diagnosis. AI-based chatbots listen to patients’ health issues and symptoms and use algorithms to guide them toward appropriate therapy. Sophisticated AI platforms analyze medical images, like those generated during radiotherapy and mammography, to detect diseases.

6. Robotic surgery with AI

Robotic surgery is an emerging field that is gaining significant attention. Through smart AI robots, surgeries can now be performed in previously inaccessible areas. Whether an operation takes 30 minutes or several hours, a robot can perform each operation consistently and meticulously. And robot performance is believed to dominate over human abilities, which tend to decline over time.

7. AI in supply chain management and logistics

Pharmaceutical companies and drug manufacturers revolutionize their workflows via the use of AI. AI simplifies demand forecasting and enables scaling production based on need.

To sum up

In this article, we’ve disclosed the benefits and challenges of implementing AI in life sciences, as well as observed seven use cases of it. 

There’s no denying that AI has transformed the way professionals approach clinical trial design, disease diagnosis, surgeries, and supply chain management. 

As AI technology continues to evolve, we can expect to see even more groundbreaking applications in the life sciences, which will continue to shape and enhance the future of life sciences.

At Kanda, we don’t get caught up in the hype surrounding artificial intelligence. Instead, we focus on translating these technologies into customized solutions that measurably address data-intensive problems. 

Our AI and Machine Learning Services are designed to assist companies in developing applications that uncover valuable data insights, monetize data, provide consumer intelligence, optimize consumer journeys, improve operational efficiency, and create innovative solutions that challenge established workflows and business processes.

Talk to experts to see how we can help you thrive in the AI-shaped world. 

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