INTRODUCTION:

Artificial intelligence is the system that perceives its surrounding environment and takes actions that increases its chance of achieving its goals. Some definitions describe that artificial intelligence is using of machines that reduced the functions that human associate with the human mind which is “leaning” and “problem solving”, but this definition is rejected by various researchers.¹ Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding, followed by new approaches, success and renewed funding. Artificial intelligence research tried and removed many of the different approaches since its founding, including simulating the brain, modeling human problem solving, learning, formal logic, large databases of knowledge and imitating animal behavior.² In the first decades of this century, highly mathematical statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.

In pharmaceutical companies they are looking for ways to leverage artificial intelligence and machine learning within the healthcare and the biotech industry. Most of the top pharmaceutical companies are collaborating with Artificial intelligence technology in their manufacturing as well as R&D of overall drug discovery. According to researchers, the use of Artificial intelligence technology improves decision making, optimizes innovation, improves efficient of research/ clinical trials, and creates beneficial new tools for physicians, consumers, insurers and regulators. Pharmaceutical companies including Pfizer, Merck, Astra Zeneca, Sanofi, Roche, Johnson and Johnson have already collaborated with or acquired artificial intelligence.^2,10

TYPES OF ARTIFICIAL INTELLIGENCE:

1. Reactive machines:

These are the oldest forms of artificial intelligence systems that have extremely limited capability. These machines do not have memory based functionality. They emulate the human minds ability to respond to different kinds of stimuli. This means such machines cannot use previously gained experiences to inform their present action that is these machines do not have the ability to learn. These machines could only be used for automatically responding to a limited set or combination of inputs.³

2. Limited memory:

These are the machines that, in addition to having the capabilities of purely reactive machines, are also capable of learning from historical data to make decisions. Nearly all existing applications that we know of come under this category of artificial intelligence. All present day artificial intelligence systems, such as those using deep learning are trained by large volumes of training data that they store in their memory to form a reference model for solving future problems.^6,11

3. Theory of mind:

This type of artificial intelligence is the next level of artificial intelligence systems that researchers are currently engaged in innovating. A theory of mind level artificial intelligence will be able to better understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes.

4. Self-aware:

This is final stage of artificial intelligence development which currently exists only hypothetically. Self- aware artificial intelligence which self-explanatory which evolved to be so akin to the human brain that it has developed self-awareness.⁹

5. Artificial narrow intelligence (ANI):

This involves most complicated and capable artificial intelligence that has ever been created to date. It refers to only perform a specific task autonomously using human like capabilities.⁴

6. Artificial general intelligence (AGI):

These systems will be able to independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for training.⁶

7. Artificial super intelligence (ASI):

This system use to replicate the multifaceted intelligence of human beings will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis and decision making capabilities.⁵

Use of Artificial intelligence in Pharmaceutical industry

Drug discovery often takes a long time to test compounds against samples of diseased cells. Finding compounds that are biologically active and are worth investigating further requires even more analysis. It is not easy but quite simple process by using artificial intelligence in it.^1,2,11

APPLICATION:

· In drug development and production artificial intelligence provides various opportunities to improve processes. Artificial intelligence can perform quality control, reduce materials waste, improve production reuse, and perform predictive maintenance. Machine learning can help forecast and prevent over- demand and under-demand as well as fix supply chain problems and failures in the production line.⁷

· Artificial intelligence and machine learning can significantly help with diagnostic assistance by providing a more data driven approach to patient categorization.

· It is easier to predict an outcome than to suggest a solution to change that outcome during the process of medical treatment.⁴

· Artificial intelligence can help optimize the medical treatment process through mobile apps with health measurement and remote monitoring capabilities. The personalized data from the apps can help to improve research and development, as well as treatment efficacy.

· It significantly helps to accelerate cancer diagnosis and treatment including in colon cancer and GI cancer.^7,9

FUTURE OF ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL INDUSTRY:

· Artificial intelligence in pharmaceutical industry shows no sign of slowing down. According to recent research, about 50% of global healthcare companies plan to implement artificial intelligence strategies broadly adopt the technology by 2025.

· Global pharmaceutical and drug development companies will invest more in discovering new drugs for chronic and oncology diseases.⁸

· Some of the major diseases that Artificial intelligence will tackle in the future include chronic kidney disease, diabetes, cancer, and idiopathic pulmonary fibrosis.

· Artificial intelligence will also shape the future of Pharmaceuticals by improving candidate selection processes for clinical trials. The tech also helps to remove elements that may hinder clinical trials, reducing the need to compensate for those factors with a large trial group.

· Artificial intelligence and machine learning will continue to help further drug discovery and manufacturing. The future will be artificial intelligence enabled.⁵

REFERENCES:

1. K. Kumar and G. S. M. Thakur. Advanced applications of neural networks and artificial intelligence: A review. International Journal of Information Technology and Computer Science. 2012; 4(6): 57–68.

2. J. M. Spector and D. J. Muraida. Automating Instructional Design: Concepts and Issues, Educational Technology Publications. Englewood Cliffs, NJ, USA, 1993.

3. T. Horakova, M. Houska, and L. Domeova. Classification of the educational texts styles with the methods of artificial intelligence. Journal of Baltic Science Education. 2017; 16(3): 324–336.

4. R. W. Lawler and N. Rushby. An interview with Robert Lawler. British Journal of Educational Technology. 2013; 44(1): 20–30.

5. Dai, C. S. Chai, P. Y. Lin et al. Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability. 2020; 12(16): 1–15.

6. J. Knox. Artificial intelligence and education in China. Learning, Media and Technology. 2020; 45(3): 1–14.

7. A. Seldon and O. Abidoye. The Fourth Education Revolution, University of Buckingham Press, London, UK. 2018.

8. J. Loeckx. Blurring boundaries in education: context and impact of MOOCs. The International Review of Research in Open and Distributed Learning. 2016; 17(3): 92–121.

9. B. Boulay. Artificial intelligence as an effective classroom assistant. IEEE Intelligent Systems. 2016; 31(6): 76–81.

10. R. Trescak, B. Yang, E. Zio, and X. Chen. Artificial intelligence for fault diagnosis of rotating machinery: a review. Mechanical Systems and Signal Processing. 2018; 108: 33–47.

11. X. Ge, Y. Yin, and S. Feng. Application research of computer artificial intelligence in college student sports autonomous learning. Kuram Ve Uygulamada Egitim Bilimleri. 2018; 18(5): 2143–2154.

Received on 07.10.2023 Modified on 21.10.2023

Asian J. Pharm. Tech. 2023; 13(4):304-306.

DOI: 10.52711/2231-5713.2023.00054