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AI in Innovation Processes an IP Puzzle to Be Solved

By Fernando Becerril - Independent Contributor
Socio Senior


Fernando Becerril By Fernando Becerril | Independent Contributor - Fri, 06/02/2023 - 12:00

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Artificial intelligence (AI) is probably the most disruptive technology of the 21st century and promises, in the next few years, to generate substantial change in many environments close to our daily lives. Since its introduction in the 1950s, AI has evolved rapidly and today, it is having a significant impact on several technological fields, including healthcare, finance, transportation, and manufacturing. In finance, AI is used for fraud detection, risk management, and investment management. In transportation, it has been used for autonomous vehicles, traffic prediction, and route optimization. In regard to manufacturing, it is used for predictive maintenance, quality control, and supply chain management. These are just a few practical examples, and there are likely many more of which we are probably not aware.

One of the industrial sectors in which AI is having a relevant impact is the health-related industry, where IA has been used for disease diagnosis, drug discovery, and personalized medicine, among other important contributions.

Particularly in the pharmaceutical industry, AI is being used throughout the drug development life cycle, from target identification to the clinical phase of products.

Two of the biggest challenges for the pharmaceutical industry are the time and costs associated with the development of new drugs. Traditionally, the development of new molecules has been a long and expensive process that can take years and cost billions of dollars. AI has substantially changed the way new drug development is conducted. Scientists now use machine learning algorithms and other artificial intelligence techniques to analyze large amounts of data, including databases of molecules and patent information, in order to be able to identify both potential targets and compounds. It is also widely used to optimize compound synthesis and to predict drug activity in biological models. While it is true that these activities were already performed using highly developed mathematical and statistical models with sophisticated computing tools, these "new" applications involving AI have accelerated the drug discovery process and increased the efficiency of researchers. According to a report by Deloitte, the use of AI in drug development can potentially reduce the time and cost of bringing a new drug to market by up to 50%.

AI is also being used in the clinical phase of drug development. Machine learning algorithms can analyze impressive volumes of clinical trial data to identify patterns and predict outcomes in a way that is not only more efficient, but effective. These new techniques help researchers design more effective clinical trials and improve the accuracy of results.

A good example of the application of artificial intelligence in the pharmaceutical industry is drug design based on molecular structure. Artificial intelligence algorithms are able to analyze the molecular structure of a disease and design specific drugs that can effectively target it. This technique has led to important advances in the treatment of diseases like cancer and diabetes. Additionally, by analyzing patient data, AI algorithms can help predict which treatments are most likely to be effective for a particular patient and reduce the risk of adverse events. A study by Nature found that AI algorithms were able to predict which cancer patients would respond positively to immunotherapy with 86% accuracy.

Another significant contribution of artificial intelligence in the pharmaceutical sector is its ability to improve the efficiency of drug production. Artificial intelligence systems can optimize manufacturing processes to reduce costs and increase product quality. This is especially important in the production of highly complex drugs, such as biologics.

Overall, AI has the potential to revolutionize the pharmaceutical industry by accelerating drug discovery, improving patient outcomes, and reducing healthcare costs.  The impact this will have on the industry is incredible, as stated by several reports and studies on the impact of AI in the pharmaceutical industry. According to a report by Grand View Research, global market size of AI in the pharmaceutical industry  was valued at US$2.06 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 43.8% from 2021 to 2028. Another report by Meticulous Research states that AI in the pharmaceuticals market is expected to reach US$25.27 billion by 2027.

While the many benefits that AI offers to the pharmaceutical industry and society in general are clear, it is also true that the use of this technology raises a number of important intellectual property challenges. The first of these is the determination of who owns the intellectual property rights in cases where AI is used to develop new drugs.  If a company uses AI to discover a new molecule, who owns the intellectual property rights to that molecule? Is it the company that owns the AI or the original creator of the AI that developed the algorithm used to discover it? Or neither?  This forces us to raise the question of whether inventions created by AI should be considered inventions.  This discussion is not new, and several protection attempts in several jurisdictions have reached judicial instances in which it has been determined that an invention where the AI itself identifies the problem and offers a solution to it, without human intervention in the process, is not subject to obtaining a patent right. Under current legal conditions, recognition as inventor or creator is limited to a natural person and the ownership of the right is only possible for a natural or legal person.  An artificial intelligence system, therefore, could not be recognized, neither as an inventor nor as a right holder.

Another challenge in terms of intellectual property is the protection of data used to train machine learning algorithms. The data is essential for training AI algorithms, but it may also be confidential or have intellectual property rights. If the data is used without the appropriate permissions, there may be infringements of intellectual property rights and a risk to privacy, an issue that is not only illegal, but also very sensitive in our times.

Finally, AI also presents challenges in terms of liability for patent infringement. If an AI algorithm discovers an invention that infringes an existing patent, it can be difficult to determine who is liable for the infringement. AI may be designed and trained by a company, but it can also make autonomous decisions that, even unintentionally, infringe existing intellectual property rights.

In conclusion, AI is fundamentally changing the way in which drugs are developed in a positive way for society and in view of this accelerated development curve, legal systems need to be updated effectively to guarantee legal certainty for all those involved in the process, from researchers and industries to end users.  It is possible that in the coming years, we will see changes in the legislation that will allow, on the one hand, the adequate protection of these types of innovations, and on the other hand, to guarantee society fast and cost-effective access to these new products and finally, as it is and has been its spirit, to promote technological development now incorporating the use of AI technologies that, until a few years ago, were only a matter from science fiction.

Photo by:   Fernando Becerril

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