Artificial intelligence; Pharmaceutical industry; Manufacturing precision; Industry 4.0; Healthcare technology; Drug discovery

Artificial intelligence (AI) has proven to be important for every walk of life and has become increasingly popular across various sectors worldwide because of its benefits and efficiency in reducing human effort and time [1]. AI has five major branches, including machine learning (ML) (learning for the data and improving over time without being programmed), deep learning (using artificial neural networks and mimicking the human brain), natural language processing (understanding, interpreting and generating human language), computer vision (understanding visual information) and robotics (performing autonomous tasks with the integration of physical mechanics) [2,3].

The pharmaceutical industry, which is a major component of the healthcare system, plays a vital role in disease prevention, prophylaxis, treatment, and research. The use of AI in the pharmaceutical sector has not only reduced human effort and human errors but also saved time, energy, and cost and has introduced a variety of horizons in drug development, clinical trials, and postmarking surveillance of pharmaceuticals [4]. Large language-based systems of AI equip pharmaceuticals with faster hypothesis generation and evidence-based decision making with the help of data in all stages of a pharmaceutical services chain [5]. It is estimated that the use of AI in pharmaceutical sciences for manufacturing, research and development will increase the pharmaceutical market from USD 0.64 billion in 2024 to 34.8 billion by 2040 across the globe [6].

AI has now simplified the drug development process, an activity that costs the industry approximately 2.8 billion USD and 10 years of time, with 90% of therapeutic molecules failing to successfully complete phase II trials [7,8]. AI helps in repurposing known drug molecules that can directly take part in phase II trials and costs the industry approximately 2.6 billion USD for drug development from scratch [9]. The application of AI for improving clinical trial design, predicting patient responses, and managing regulatory documentation potentially reduces the development time by 25–40% and the annual cost of the pharmaceutical industry by 100 billion USD [10].

The integration of technologies such as “Industry 4.0” and “Quality by Design” (QbD) has reshaped pharmaceutical manufacturing by streamlining production through automation, standardization, quality control and risk management. Many pharmaceutical manufacturing units have incorporated QbD to gain a comprehensive understanding of product production processes [11,12]. A computer-aided program helps to effectively resolve formulation design-related problems and improves the stability of the finished products [13]. AI algorithms not only help to enhance the quality of service but also optimize production processes via effective quality risk management [14,15]. Recent studies highlight how AI-driven process control, predictive maintenance, and real-time monitoring are transforming pharmaceutical manufacturing compliance under good manufacturing practices (GMPs), while regulators are developing new frameworks to ensure the safe and transparent use of AI in production [16,17].

Despite the benefits of AI integration in the pharmaceutical sector, the availability of reliable datasets, lack of trained professionals in information technology, financial constraints, fear of job displacement and the “black box” aspect of AI pose a challenge for the pharmaceutical industry to AI in the routine operations of the industry [18]. Furthermore, no AI-developed drug has yet fully obtained regulatory approval from authentic international regulatory agencies [19,20]. To gain the complete benefits of AI in pharmaceutical manufacturing and research, overcoming these major barriers with intersectoral coordination and policy development is important [21,22]. Ethical issues such as issues of data privacy, algorithmic bias, and concerns of job replacement need to be addressed to ensure the worthwhile and appropriate use of AI in pharmacies [23].

In summary, AI is considered an incentive for pharmaceutical innovation, as it responds to major challenges such as drug development costs, time, safety and manufacturing accuracy. However, successful integration involves overcoming quality problems in data, a lack of trained experts, and ethical issues. The growing evidence base and rapid technological advances in the contemporary world strongly justify AI’s transformative role in achieving faster, safer, and more cost-effective pharmaceutical development, supporting the argument that AI integration is not only beneficial but also essential for the pharmaceutical industry’s future [24]. By strategically integrating AI into pharmaceutical manufacturing and research, the industry can achieve significant advancements, enhancing global healthcare.

The editorial was written and revised by the author herself.

This research received no specific grant from the public, commercial, or not-for-profit funding agencies.

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The author declares no conflicts of interest.

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