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The increasing need for AI in the pharma industry

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By Meena Villa Vij, Vice President, Digital, Data & IT, Novo Nordisk Global Business Services and Francis Prashanth Anandan, Lead, Digital Innovation Hub, India, Novo Nordisk Global Business Services

The pharmaceutical industry has been on an accelerated path to scientific advancement. A human-centric approach to drug discovery combined with core competencies and technology platforms drive future innovation. Technologies including Artificial Intelligence (AI) and Machine Learning (ML) are powering major breakthroughs and innovations in the healthcare sector.

Organisations are exploring and adopting digital technologies to help improve top and bottom-line growth by reducing the time to conduct clinical research, accelerating the research on therapies to clinical trials, improving operational excellence, and reducing risk.

According to a survey conducted by Precedence Research, the worldwide market for AI in the pharmaceutical industry is expected to experience substantial growth, with an estimated increase from USD 905.91 million in 2021 to USD 9.24 billion by 2030. By tackling significant obstacles and propelling advancements in the field, AI holds the potential to revolutionise the pharmaceutical sector. The adoption will enable faster and scaled use of AI in drug discovery, leading to more breakthrough innovations, and efficiency gains to better serve the needs of the patient.

Here is how AI and data are being used in the pharma industry.

1. Accelerating drug discovery
AI algorithms analyse vast amounts of biological and chemical data to identify potential drug candidates. Machine learning models help scientists predict the effectiveness of a drug, optimize chemical structures, and simulate interactions with biological targets. This accelerates the drug discovery process, reducing costs and improving efficiency. Novo Nordisk recently partnered with Microsoft for AI-driven drug discovery.

2. AI-driven clinical trials
AI-powered technologies enable the identification of evidence-based patient populations for clinical trials, leading to more accurate and targeted recruitment. Predictive analytics and machine learning algorithms analyse real-time patient data, allowing for faster identification of adverse events and more effective monitoring of drug efficacy. This improves trial efficiency, increases patient safety, and expedites the development of new therapies.

3. Developing personalised healthcare
AI algorithms are being used to analyse patient-specific data, including genomic information, electronic health records, and lifestyle factors, to tailor treatment plans to individual needs. This enables the identification of biomarkers, prediction of disease progression, and optimisation of therapy selection, dosage, and timing.

4. Pharmacovigilance and drug safety
AI is helping drive improvements in reporting and monitoring therapy effectiveness by efficiently detecting adverse events directly from patient electronic health records and other primary sources. AI-powered algorithms reduce manual errors while monitoring real-world data to detect adverse drug reactions, identify potential drug interactions, and assess drug safety. Natural language processing and machine learning models examine social media, electronic health records, and scientific literature to provide early warnings and improve post-market surveillance, enhancing patient safety.

5. Streamlining production and distribution
The supply chain has seen unprecedented disruptions in the last few years making AI an important element in optimising the ever-complex pharma supply chain. AI technologies automate demand planning, supply chain, and marketing data to predict demand & supply and provide recommendations. It can also be leveraged for using advanced analytics that will help streamline production & distribution.

6. Enhanced commercial teams

With the addition of AI, commercial pharma teams are developing more targeted marketing strategies in a more targeted manner and converting massive amounts of unstructured data sources to gain rich insight into how consumers are making decisions. Beyond marketing, AI is also being used to analyse and resolve patient grievances and queries. Conversational AI is used to increase patient literacy while providing them with better treatment options.

The pharma and healthcare industries are witnessing unprecedented transformation due to the increasing integration of AI technologies. These advancements hold promise for the development of safer and more effective therapies, ultimately leading to improved patient outcomes. As the industry continues to embrace AI technologies, it holds the potential to transform healthcare by accelerating the development of new therapies, optimizing treatment plans, and improving patient outcomes. With its promising impact on both scientific advancements and business operations, AI is paving the way for a more efficient, effective, and profitable future in the pharma industry.

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