AI in the chemical and pharmaceutical industry: 3 topics to keep track of

Topic n°1: AI predictive capabilities enable faster and more efficient processes along the company value chain

Analyzing large amounts of data and helping humans to make sense of it is the bread and butter of machine learning applications. Predictive analysis can support pharmaceutical industries in different applications; from optimizing pre-clinical R&D experiments, to quickly analyzing results in clinical phases, to streamlining production processes, and even managing projects at corporate level.

AI to optimize the number of experiments to be performed in pre-clinical research

Among the showcased applications, we saw for example how machine learning approaches can support pre-clinical research, by optimizing the number of experiments to be performed when testing compounds and molecules in new formulations. This effectively reduces both the time and the costs of the initial phases of research, while still providing sufficient information to choose the best candidates to bring forward to subsequent development phases.

AI to analyze medical imaging

We were also shown how machine learning algorithms are being used to screen medical exam results by analyzing the data for anomalies or errors. If issues are detected, not only the examiner could be alerted directly, but this also ensures that only accurate and valid exam results are sent to the physicians for further analysis and reporting.

Read more: AI in medical imaging, a revolution in medical diagnosis and patient care

AI to analyze of the status of tools and machinery

In the production line, AI can be used for the predictive analysis of the status of tools and machinery: by using sensors combined with AI software, we can predict when a piece of equipment is about to need to be checked. This enables more precise and necessary maintenance compared to the currently widespread preventive methods, which rely on regular intervals – often either too long or too short, depending on specific cases – for checking and verifying machinery.

Topic n°2: the challenge of developing regulatory frameworks for rapidly advancing artificial intelligence

As AI continues to evolve, regulatory agencies face the daunting challenge of developing regulatory frameworks that can keep pace with the rapid advancements.

Unlike traditional software, which operates based on predefined instructions and remains static until explicitly updated, artificial intelligence systems have the ability to learn from data and evolve their behavior over time. This adaptive nature complicates the regulatory aspects of any process or device embedded with AI functionalities, as these systems could potentially change autonomously and unpredictably post-deployment. While this means we cannot simply look at the quality of the output to regulate AI systems, creating rules for an evolving system is a challenging task that regulatory agencies are still working on.

Two key aspects of artificial intelligence solutions were particularly underlined when discussing what is relevant for regulations:

1. Data quality: using high quality data to train AI systems is pivotal to obtain solid and trustworthy outputs. This aspect is a requirement both from the regulatory point of view – to ensure the accuracy of the systems – and from the productivity point of view – since high quality input data lead to more effective and performant models.
2. Training methods: regulatory bodies will want to know how AI systems are trained and tested, to ensure they cannot deviate from their objective.

An example of regulatory effort incorporating these aspects is the FDA’s Good Machine Learning Practice (GMLP) for medical device development. GMLP provides a framework based on 10 guiding principles, emphasizing the importance of multi-disciplinary expertise to ensure robust and secure engineering, the need for diverse and representative training data and transparency in AI models, and real-world clinical testing. Furthermore, it highlights the significance of post-deployment monitoring coupled with feedback mechanisms to ensure ongoing safety and efficacy.

Especially in the highly regulated pharmaceutical field, the introduction of regulations and guidelines will be needed to both foster and control the use of AI systems.

Learn more about how our team can support you in your artificial intelligence projects >

Topic n°3: the role of artificial intelligence in sustainability

Artificial intelligence has the potential to contribute to sustainability efforts within any industrial sector, including the chemical and pharmaceutical ones. By optimizing processes, AI can help companies adopt greener practices and reducing waste. For example, AI can assist in selecting more sustainable raw materials or solvents for chemical reactions, optimizing energy use in manufacturing processes, and refining operations to minimize energy waste.

However, the sustainability of artificial intelligence itself must also be considered. The deployment of AI technologies requires substantial computational power and the storage of large amounts of data, which can lead to increased energy consumption and environmental impact. This is in line with Kate Crawford’s article from Nature where the author, citing Sam Altman (CEO of OpenAI), reports how newer and more powerful AI systems will have an incredibly high power demand.

To support companies in understanding their environmental impact, initial efforts are also being made to estimate the CO2 emissions of their computational operations (such as: ML CO2 Impact or CodeCarbon).

As artificial intelligence becomes more prevalent in any work environment, it will be crucial to develop energy-efficient algorithms and invest in renewable energy sources to power data centers, ensuring that the benefits of AI do not come at an unsustainable environmental cost.

Artificial intelligence is poised to transform most of our industries, offering enhanced predictive capabilities, driving the need for new regulatory frameworks, and presenting both opportunities and challenges for sustainability. A final point was raised on ethical grounds: how much should we fear artificial intelligence ? How much will it be safe to trust it? Will AI reach a level of intelligence that we may define “conscious”? We will need to address these considerations as we integrate AI into all areas of life and industry, making it crucial to balance innovation with responsible practices to manage the potential societal impacts. Alcimed can support you in exploring the opportunities of artificial intelligence in your field, and in mastering this rapidly evolving technology. Don’t hesitate to contact our team!

About the author,

Lorenzo, Senior Consultant in Alcimed’s healthcare team in Italy