It’s no novelty that many pharma companies have been integrating technology elements into their workforce. AI and machine learning are no strangers to the many solutions in this spectrum. Without a doubt this increased efficiency to more than one front in the pharmaceutical industry. Yet, what can the challenges pose for pharma in the long run?
The Advantages of AI and Machine Learning in the Long Run
No doubt that AI and machine learning are tech elements that are transforming the industry. These two have contributed to actions such as commercialization, early drug discovery, and clinical trial design.
Without any shade of a doubt, many pharma tech executives are having the common sense to assist in their company’s strategic direction. As more and more processes become automated, these technology elements evolve in new ways every day.
There’s one good reason why AI will have a huge role to play in pharma. To this sector’s great fortune, they’ve had great lists of data. With this new tech option, many organizations will be able to extract value that will provide them with automated possibilities within the next 10 years.
The highlight of pharma came only during the COVID pandemic. In the last two years, many Big pharma companies stumbled upon this new solution. With building pressures and problems left by the pandemic, it was no wonder that this came as a “miracle solution” for many. Not only did it help speed up processes for drug discovery, but it also helped to enhance commercialization on many fronts.
AI and machine learning will be taking data details to a whole new level. Right now, most of the investments on the end of pharma for these solutions are very centred around drug formulation. However, there’s a potential yet to be explored in regards to sales enablement, for example. Yet some pain points are getting in the way of further progress.
The Start of Some Pain Points
With all evolution come inevitable challenges. AI and machine learning are a work in progress in more than one sector. In the case of pharmaceutical companies, many of them are still testing or discovering the new potential for these tools. Here are some of the main pain points that will come their way, while discovering new and improved ways to tackle these elements.
Data’s Quality
There’s an inherent bias surrounding AI, even though many data sources have been accessed by pharma for years. Unfortunately, AI and machine learning will be as good as the data companies work with.
Should that data be biased, then the actions and eventual results will be influenced by this. It may cause some problems when it comes to handling targeted results. The more specific the data set is, the better the AI will learn from it.
For example, according to PharmaVoice, one of the fields affected by this may be medical imaging. If the images used contain only images of Caucasian patients, then the AI will be accidentally trained to diagnose a certain disease only in that race.
Another issue within this topic has to do with undefined data sets. There’s a growing need to create fixed parameters to avoid issues. Fortunately, some companies are working on providing useful and cohesive data sets.
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Blind Trust
No doubt that AI and machine learning have made many processes a lot easier for serval workforces in healthcare. But at the end of the day, these are machines that don’t think with a complex process like that of a human mind.
In that sense, avoid blind trust in these technologies, especially if they’re still adapting to a company’s data. Back to the biases matter, it’s recommended that there’s a human eye to revise this element, as well as the processes left in the hand of technology.
A little critical thinking on the end of the workers who monitor the results and conclusions produced by the company will be vital in this sense. There’s nothing wrong to admit that computers don’t have everything covered for us humans.
Using the Same Data Sets All the Time
Adopting AI and machine learning is a great step for pharma companies. However, if they continue to use the same old data sets, then we have an obstacle to progress.
By using the same sets, the results will inevitably lead to the same conclusions. In R&D (Research and Development) this can pose an issue for evolution. It will be more likely that many R&D projects are common among different companies, or that they will try to chase the same drugs for the same patients.
As mentioned previously, one thing the human mind has, that AI and machine learning don’t, is critical thinking. If companies rely heavily on the same data, then the results won’t be as innovative as they expect. It aggravates the situation if the data is also under influence of an algorithm that makes the data inaccurate.
Biology VS AI
Lastly, there’s also the small fact that biological data sets are still not 100% easy to interpret by AI. Even though this is a work in progress, for the time being, this will have to be a detail that will still need human monitoring for now.
In clinical trials, for example, as soon as there’s progress in this regard, AI and machine learning will prove to be useful. Usually, the inefficiencies in these procedures are detected around phase 2 of the process. When the testing on patients begins it will reveal the efficiency of the drug. AI still doesn’t have a complete understanding of this type of result.
Without a doubt, there may be future progress regarding AI’s understanding of biological factors. Many expect that one of the future strong points of this technology will centre on solving issues in this field.
Conclusions
The full potential of AI and machine learning is yet to be discovered. It will likely evolve into spectrums that are unimaginable today. Even though a machine will never replace a human mind, this proves that there’s always a space for improvement with the intervention of a real person. More and more uses will be discovered as time marches on, and with the intervention of human hands, new possibilities will be created in the future.
