A Review on Artificial Intelligence

Abstract

Artificial Intelligence (A.I.) is a multidisciplinary field whose goal is to automate activities that presently require human intelligence. Recent successes in A.I. include computerized medical diagnosticians and systems that automatically customize hardware to particular user requirements. The major problem areas addressed in A.I. can be summarized as Perception, Manipulation, Reasoning, Communication, and Learning. Perception is concerned with building models of the physical world from sensory input (visual, audio, etc.). Manipulation is concerned with articulating appendages (e.g., mechanical arms, locomotion devices) in order to effect a desired state in the physical world. Reasoning is concerned with higher level cognitive functions such as planning, drawing inferential conclusions from a world model, diagnosing, designing, etc. Communication treats the problem understanding and conveying information through the use of language. Finally, Learning treats the problem of automatically improving system performance over time based on the system\'s experience. Many important technical concepts have arisen from A.I. that unify these diverse problem areas and that form the foundation of the scientific discipline. Generally, A.I. systems function based on a Knowledge Base of facts and rules that characterize the system\'s domain of proficiency. The elements of a Knowledge Base consist of independently valid (or at least plausible) chunks of information. The system must automatically organize and utilize this information to solve the specific problems that it encounters. This organization process can be generally characterized as a Search directed toward specific goals. The search is made complex because of the need to determine the relevance of information and because of the frequent occurence of uncertain and ambiguous data.

Introduction

Artificial intelligence (AI), or machine intelligence, enables computers and machines to perform human-like tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI is classified into types based on capability—from reactive machines and limited memory systems to theoretical forms like theory of mind and self-aware AI. Narrow AI, the form commonly in use today, focuses on specific tasks like facial recognition, autonomous driving, and internet search, while the long-term goal is general AI, capable of performing all human cognitive tasks.

AI has transformative applications in healthcare, including patient record management, treatment planning, repetitive diagnostic tasks, genomics, drug discovery, and personalized medicine. Systems like IBM Watson, Deep Genomics, and Atomwise leverage AI to analyze large datasets, optimize treatment plans, accelerate drug development, and enhance clinical trials. In pharmaceuticals, AI accelerates drug discovery, improves clinical trial efficiency, optimizes supply chains, and enhances pharmacovigilance.

The benefits of AI include faster drug development, cost reduction, personalized therapies, and improved healthcare efficiency. Challenges include regulatory hurdles, algorithmic bias, model transparency, and the “black box” nature of some AI systems. The future of AI in healthcare and pharmaceuticals involves continued innovation, broader adoption, and integration with clinical and regulatory processes.

Conclusion

AI involves the combination of human knowledge and resources with Artificial Intelligence. As research into AI continues, with many interesting applications of it in progress, one may consider it a necessary evil even for those that see it as an enemy. Therefore, it is strongly recommended that pharmacists should acquire the relevant hard skills that promote AI augmentation. Education about and exposure to AI is necessary throughout all domains of pharmacy practice. Pharmacy students should be introduced to the essentials of data science and fundamentals of AI through a health informatics curriculum during their PharmD education. Pharmacists must also be allowed to develop an understanding of AI through continuing education. Data science courses or pharmacy residencies with a focus on AI topics should be made available for pharmacists seeking more hands-on involvement in AI development, governance, and use. As these technologies rapidly evolve, the pharmacy education system must remain agile to ensure our profession is equipped to steward these transformations of care.

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Copyright

Copyright © 2025 Arvind Singh, Pramod Mishra, Sujeet Pratap Singh, Dr. Tarkeshwar P. Shukla. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.