Like it or not, artificial intelligence (AI) has already begun to reshape the way pharma and biotech companies operate at almost every level. The integration of AI-based data analysis into the entire spectrum of these companies’ activities has led to increasing collaboration with AI developers, and we expect this trend to continue as large and small organizations seek to harness the latest innovations in this rapidly-changing technology space.

For those that may be unfamiliar, AI generally refers to the simulation of human reasoning, analysis, and intelligence processes by specialised computing algorithms. These algorithms process large, complex, and wide-ranging datasets to predict outcomes and results, identify patterns and trends, and gain actionable insights much faster and more efficiently than traditional data analysis. Of course, solving problems using these types of datasets is at the core of innovation in the life sciences and healthcare industries. Therefore, the immense amount of R&D, clinical, and patient-centric data that is continually being generated as new drugs and therapies are developed and brought to market is a perfect match for the pharma, biotech and digital health landscape.

We have seen companies utilise AI-based solutions in several exciting areas, including drug discovery and clinical development, patient diagnosis and personalised treatment, and even customer relationship and marketing strategies.


Research and development of drugs and therapies is the most expensive and inefficient process that pharmaceutical firms face, yet one that must be navigated in order to generate new products that drive the business forward. Much of the time and cost spent during drug discovery happens early on, when researchers try to uncover targets for a voluminous library of potentially-applicable molecules. Errors made during this phase can lead to failure of the drug during clinical trials, and delays in identifying compelling targets can put companies at a competitive disadvantage.

AI will benefit this process by enabling faster and more reliable identification of viable drug targets through the use of AI-based systems that are trained using volumes of existing clinical data both to design new molecules and predict the binding affinity and potency of these molecules for particular targets. As a result, we expect AI to streamline the early stages of R&D, enabling researchers to focus on the most promising leads and to discover unexpected outcomes – which could potentially lead to improved success rates during clinical trials.

Another facet that AI-driven technology will impact is the identification and categorisation of various biological processes, including disease states and biomarkers, in the context of evaluating the efficacy of drugs under development. AI systems are well-suited to the rapid analysis and classification of data in other contexts (such as content recommender systems), and life sciences companies are beginning to adapt these same techniques to biological data – such as those that may be generated from tissue samples and cell assays – in order to detect the presence of elements that may contribute to a present or future disease state, predict treatment response of certain compounds, and identify common disease / response factors across a varying population of patients.


A second promising area for the application of AI-driven technology is the diagnosis of disease in patient populations and development of personalised treatment options. Traditionally, the diagnosis of disease from medical imaging has been performed by radiologists and other trained medical professionals. AI provides the opportunity to automate this task by leveraging the power of existing image processing algorithms that detect patterns and structures in image data. An AI system can be trained to recognise and label images that contain potential signs of disease or other abnormalities, including elements that a human may be unable to detect.

In addition, the sheer amount of patient information that is available to life sciences companies – including electronic health records, digital health data from personal sensors, wearables and other smart devices, genetic features, and environmental data – provides the perfect opportunity to implement AI systems which can rapidly comb through this data in order to generate specific care options and recommendations. One example is the creation of precision medicine that is tailored to a patient’s unique biological makeup and lifestyle, with the intent to provide the best possible outcomes. We expect biotech and pharma companies to continue to adopt and develop AI technology that works to address each patient’s particular needs.


Even activities such as customer relationship management and marketing stand to benefit from AI-based techniques and platforms. AI will allow companies to conduct in-depth, multi-faceted data mining and analytics using industry, health care provider, and patient data. These results enable life sciences firms to identify actionable trends in customer and provider experience, engagement, and sales impact. The companies that can take advantage of these insights will be able to provide better tools to their sales and marketing teams for driving adoption of new products in the market.


Of course, the above opportunities for AI to play an integral role in the operation of pharma and biotech companies do not come without challenges. Most significant of these is the necessity to coalesce and adapt existing sources of data so that they are usable by current and forthcoming AI technologies. Many of these data sources may reside in disparate legacy computer systems (or perhaps even in non-digital form), so firms must recognise and account for the time and expense required to modernise their data for AI. Also, AI is still far from a completely automated, error-free technology solution – human oversight and validation of results generated by AI systems will be essential to avoid relying on inaccurate or defective reasoning.

Also, many life sciences companies lack the in-house expertise in the form of software engineers and data scientists that are capable of developing, implementing, and refining customised algorithms and modules using current-generation AI technology. In view of this gap, we have seen an increase in partnerships between leading pharmaceutical and biotechnology companies and smaller software developers that specialise in medical AI. We expect this trend to continue in the form of expanded collaboration and mergers or acquisitions, as life sciences companies look to keep pace with cutting-edge developments in the field and construct internal teams that can create proprietary AI platforms.

Finally, protection of the key intellectual property behind these innovations will be paramount to realising an immediate and long-term competitive advantage. Companies should thoroughly investigate whether any aspects of their in-house AI solutions might be eligible for patent, copyright, and/or trade secret protection, and if so, actively pursue these rights. Also, any joint development or collaboration agreements with third-party software or services providers should be carefully reviewed to ensure that any applicable IP rights are owned by the organisation.

Over the past five years, advances in computing hardware, cloud-based software development, and big data aggregation have made AI technology increasingly viable for applications in the healthcare and pharmaceutical industries. As many industry leaders have begun to evaluate and implement AI-based solutions, it is clear that AI is a big part of the future. In order to stay ahead of the competition, investment in and adoption of this technology at every level of the organisation will be critical.

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