The revolutionary role of artificial intelligence in biomedical research

Imagine a dedicated researcher, immersed in a laboratory filled with humming machines, scattered glassware, microscopes and petri dishes. Using these tools, this devoted researcher aims to solve nature’s greatest puzzles. They have dedicated their life to the honorable pursuit of discovering the secrets of diseases in the hopes of developing life-saving drugs.

Despite the endless commitment, a harsh reality weighs on the researcher’s mind: less than 0.1% of drug compounds make it to clinical trials. Every setback in the laboratory means more than just disappointment, it is a sign that the researcher must return to the drawing board and spend countless hours going over information in search of elusive patterns that could unlock the next breakthrough.

The researcher knows that the stakes are high and that every setback slows the race to save lives; however, the researcher perseveres to make a meaningful impact in medical science.

In the world of biomedical research, scientists such as the one described above, attempt to understand complex pathways to discover new therapeutics every day. The general public is only aware of the successes that come from the biomedical research world, while researchers who live in this world are more aware of the trials and tribulations that come with novel discoveries.

Traditionally, biomedical research is a labor-intensive endeavor focused on generating hypotheses, analyzing data and interpreting results. Artificial intelligence (AI) is poised to transform this process.

We may all be familiar with the impact AI has made on society, and its influence on biomedical research has proven to be revolutionary. AI encompasses advanced technologies and algorithms that can be used to enhance the capabilities of researchers in the pursuit of discovering the secrets of diseases and developing novel treatments.

"As researchers explore complex data, AI can act as a collaborator and expedite their path towards understanding the data."

-- Max Cook

In the hands of a biomedical researcher, AI can be used to analyze data, uncover patterns crucial for identifying novel therapeutic targets and accelerate the drug discovery process. However, there are some disadvantages and challenges that researchers may face when using AI. Navigating these advantages and disadvantages will require careful consideration by the researcher to ensure AI is being used effectively.

As the field of biomedical research advances, the data become increasingly more complex and difficult to process. This is especially true when analyzing large datasets, such as proteomics or genomics data. These datasets allow researchers to study the entire set of proteins produced by the cell or the entire set of genes within the genome of a cell, respectively. While proteomics and genomics data can be extremely useful for scientists to have a global understanding of the cell under normal and disease conditions, they can also be a nightmare for researchers to analyze. Analyzing these immense datasets can be very time-consuming and slow down the progress of a research project. Not only can AI shorten the time to process datasets, but it can also identify patterns and correlations that the human eye may miss. The identification of these novel trends could lead to a potential therapeutic target that may have never been recognized without AI.

AI can also integrate data from various sources faster and more efficiently than humans. Making connections between multiple data sources allows researchers to have a more comprehensive view of the system. AI’s ability to uncover complex relationships across disease states will help reshape our entire understanding of biological systems. This enhanced level of data integration and understanding could pave the way for quicker discovery times for impactful interventions.

The use of AI in analyzing data and identifying novel drug targets is in and of itself revolutionary. But AI can also accelerate the time-consuming and costly traditional approach of getting new drugs to the clinic.

Currently, when researchers begin the drug discovery process, they often test vast numbers of potential candidates, sometimes more than 100,000. AI has been able to improve this process by predicting the properties of these compounds to quickly narrow down the candidate pool. This is a more cost-effective and resource-efficient approach to identifying novel drug candidates. Additionally, AI can use basic and clinical datasets to predict the drug candidates' efficacy and safety, reducing the chance that the drug will fail in human clinical trials.

Although the advantages of using AI in biomedical research have the potential to rapidly transform the biomedical landscape, there are several points of caution that scientists need to be aware of when using AI. These disadvantages pertain to AI’s ability to elucidate how it got to a certain result, the reliability of the results and standardization across AI platforms. 

"AI encompasses advanced technologies and algorithms that can be used to enhance the capabilities of researchers in the pursuit of discovering the secrets of diseases and developing novel treatments."

-- Max Cook

Despite AI expediting data analysis and drug discovery, how it derived those conclusions can be difficult for researchers to understand. Understanding how a conclusion is made is crucial for biomedical researchers to make informed decisions in the future.

Another point of caution when using AI is that the quality of AI’s analysis is dependent on the quality of the data that it is given. Inaccurate datasets can cause misleading results, sending researchers in the wrong direction. AI algorithms can also be dangerous if the input data has biases. These biases will be intensified and cause unreliable results. Additionally, some AI models may perform well on certain sets of data, but struggle when trying to analyze novel data. These results could reduce the reproducibility of research findings and create misleading data.

With the increasing use of different AI platforms, researchers should be cautious about potential discrepancies in the way AI is used in research across universities. Implementing AI can be very expensive, which may allow for larger institutions to have more advanced AI than smaller institutions. It also takes time to become skilled enough to apply AI to research. Smaller institutions may not have the resources or personnel to take advantage of AI. AI could have a major impact on increasing this gap between the capabilities of universities based on funds and size. This lack of standardization across research institutions could make it challenging to compare results.

Now picture our intrepid researcher from earlier integrating AI into their workflow. As the researcher explores their complex data, AI can act as a collaborator and expedite the researcher’s path towards understanding the data. AI can serve as a tireless assistant to the researcher by constantly working to uncover subtle patterns and correlations that the fatigued scientist may have missed. With the power of AI, the researcher is no longer forced to manually analyze every piece of data, instead, they can focus on interpreting the results and finding meaningful insights.

In the biomedical landscape, the delays in new drug development will be lessened as AI accelerates the drug discovery process. Despite the revolutionary role of AI as a tool in research, it is crucial to approach the integration of AI into a scientist’s workflow with caution. Careful considerations must be followed as researchers utilize the benefits of AI on the journey to discovering scientific and medical breakthroughs.