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How AI can help to develop medical treatments for those who can't pay much.

By Susanne FeldtUpdated Apr 25, 2022 | Published Jan 1, 2021

How AI can help to develop medical treatments for those who can't pay much.

Artificial intelligence is a powerful tool for increasing the speed and efficiency of medical research, which lead ultimately to better health outcomes for all. But in order to get the most out of AI, large and complete datasets are needed. Currently, the system is not designed for providing data accessibility. Here is where human-centered design strategies, combined with legal engineering can help. I will explore this in my next article. This article explains why we need AI in medical research, especially in drug development (R&D).

Situation/ Complication

Pharma has the potential to save the world. But instead of doing so, they are developing only for diseases with a market cap; meaning they investigate only in conditions affecting the wealthy countries/population, excluding significant diseases in underdeveloped regions. This is not only fair, it sounds so evil. But, R&D is very expensive, and Pharma needs to work economically. So we should improve processes to decrease R&D cost and help to innovate faster, also for the poor and fewer spread diseases.

Solution

But what if R&D could be much faster, cheaper? If we decrease cost, we help to innovate faster allow to develop treatments cheaper. This would affect not the ones with less economic power and the development of rare diseases. And this is where the potential of AI and large public data sets lie. A study by McKinsey concluded that at least 25% of the product development cost could be cut by using AI.


The potential of AI

With the support of cloud computing, AI is helping in several different ways by drug companies of all sizes, from large multinationals to early startups to discover new drugs and make the drug discovery process more efficient. Machine learning can help companies understand the impact of drugs on specific market segments. This allows them to adjust their strategy based on disease density or patient lifestyles.

AI or rare diseases
The use of AI in drug discovery is a strong one for healthcare; today, rare diseases hardly get tackled by researchers as they need to calculate the economic value. 85% of the R&D budget is spent developing drugs for conditions affecting 15% of the population. The potential of AI-powered screening is enormous, and some are predicting that it could reduce the time needed to find a successful drug treatment from 12 years to just two. It is so important in drug discovery that there are secure gates: testing it for a different type of disease, genomic level, genetic diversity.

AI for new drug discoveries
Researchers are using AI to investigate the genetic changes associated with different kinds of disease and the effects of experimental drugs. They can then use data to explore associations between genes or discover adjacent diseases. Oftentimes the given gene affects more than one condition.

AI for individualized drugs discoveries
We will soon be able to customize medicine for each individual truly. The FDA, the Food and Drug Administration launched a precision program on using data to accelerate drugs. Also, the National Institutes of Health are pitching a “All of Us”- campaign to collect data. The donation program is based on genomic data and includes environmental facts combined with health records. Every citizen of the United States can donate their data to research. In order to design tailored drug treatments. The UK Biobank has one of the largest collections of genomic data, and they have a sophisticated approach to opening up different levels of data. Also, other companies have created projects that offer publicly collected data for the benefit of all. A prominent example in Germany is data4life and their Covid research.

AI for optimized drugs discoveries
AI has the potential to unearth correlations between disease and treatment that can increase the chances of a successful product launch. And hereby make many aspects of healthcare more efficient, enabling more people to get better care at a lower cost. Researchers can predict the outcomes of events, and predictions are made based on patterns, trends, and exceptions found in historical and current data. This can lead to the identification of both risks and opportunities. Imagine that your doctors can predict how many of their patients will survive any given course of treatment.


Conclusion

Therefore we need to create a systematic change in how we collect and share more health data, even with the so-called "evil" pharma industry. This helps us all to get healthier and democratize health globally.

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