Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

138 

 
https://internationalpubls.com 

Pharma in The Digital Era: The Role of Artificial Intelligence in Drug 

Development 

 

Dr. Priya Vij1, Patil Manisha Prashant2 
1Assistant Professor, Department of CS & IT, Kalinga University,Raipur, India. 

2Research Scholar,Department of CS & IT, Kalinga University,Raipur, India. 

 

Article History: 

Received: 09-09-2024 

Revised: 17-10-2024 

Accepted: 27-10-2024 

Abstract:  

The integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a 

transformative era, where technology reshapes traditional drug development processes. This 

paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug 

discovery, testing, and approval. Existing methods in drug development often suffer from 

high costs, extended timelines, and high attrition rates in clinical trials, posing significant 

barriers to timely patient care. To address these issues, we propose the Drug Development 

based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and 

machine learning models. The DD-AI framework enhances the identification and 

optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient 

treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, 

simulate their interactions, and streamline the clinical trial process by identifying the most 

promising candidates. Implementing the DD-AI framework has demonstrated substantial 

improvements in reducing drug development costs and timelines while increasing the 

accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, 

optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework 

offers a robust solution to existing challenges in drug development, fostering a more 

efficient, cost-effective, and patient-centered approach. The findings underscore AI's 

transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting 

patients with faster and more precise medical solutions. 

Keywords: Drug Development, Artificial Intelligence, Patient Treatment Plans. 

 

1. INTRODUCTION 

AI can help with rational drug design, decision-making, determining the right therapy for a patient, 

and managing and using generated clinical data for future drug development, it is easy to imagine its 

involvement in the entire pharmaceutical product life cycle [1]. With the use of machine learning 

algorithms and an intuitive user interface, E-VAI generates analytical roadmaps based on 

competitors, important stakeholders, and current market share [23]. These roadmaps then predict the 

key drivers of pharmaceutical sales, allowing marketing executives to reinvest in areas with poor 

performance and maximise market share [3]. This paper provides a concise overview of the many AI 

applications used in the pharmaceutical industry [17]. The pharmaceutical business is one of several 

that has seen a rise in the use of artificial intelligence [5].  

To reduce human workload and achieve targets in a short period of time, AI is being used in various 

sectors of the pharmaceutical industry [18]. In this paper, DD-AI focus on drug discovery and 

development, drug repurposing, improving pharmaceutical productivity, and clinical trials, among 



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

139 

 
https://internationalpubls.com 

others [7]. Along with the future of AI in the pharmaceutical sector, it covers crosstalk between the 

tools and methods used in AI, current obstacles, and solutions to solve them [19]. Acquiring, 

analysing, and utilising such information to address complicated healthcare issues becomes more 

challenging with digitalisation [4] [9]. This is why AI is being used; it can automate processes and 

manage massive amounts of data [8] [20]. AI refers to a technological system that uses a variety of 

sophisticated tools and networks to simulate human intellect [12]. However, it does not pose a 

serious danger of totally displacing the need for human interaction [11].  

The main objective of this paper is as follows: 

• To cut down on time and money spent on early stages of drug development by using AI-

driven algorithms to find and optimise possible therapeutic candidates. 

• The goal is to streamline the trial process, reduce attrition rates, and speed up medication 

approval by using machine learning models for clinical trial success prediction, candidate 

identification, and streamlining. 

• The goal is to use AI's predictive skills to personalise therapies, making them more effective 

while reducing side effects, and ultimately putting the patient at the centre of pharmaceutical 

operations. 

The remaining of this paper is structured as follows: In section 2, the related work of drug 

development is studied. In section 3, the proposed methodology of DD-AI is explained. In section 4, 

the efficiency of DD-AI is discussed and analysed. Finally, in section 5 the paper is concluded. 

2. RELATED WORK 

Data issues, including data size, variety, growth, and unpredictability, pose serious problems for AI. 

Pharmaceutical businesses sometimes have data sets that include millions of substances; 

conventional ML systems may struggle to handle this volume of information [14]. A computer model 

based on quantitative structure-activity relationships may rapidly forecast a large number of 

chemicals or basic physicochemical characteristics like log P or log D. But these models can't even 

begin to anticipate simple biological features like a compound's effectiveness or side effects. Small 

training sets, experimental data errors in training sets, and an absence of experimental validations are 

other issues that AI-based models encounter. 

Pharma and Bio-Technology in Digital Health (P-BT-DH):Health outcomes and healthcare 

delivery may be enhanced by digital health interventions, which include the use of digital technology 

and linked devices [2]. This encompasses a wide range of digital health technologies, such as 

telemedicine, EHRs, wearables, and mobile health apps [10]. Consequently, a number of research 

and development initiatives across disciplines are picking up steam. As an example, the medical 

devices industry is seeing a surge in the development and implementation of digitally enabled smart 

biological materials and medical equipment by Kasoju, N. et al., [21] Several phases of medication 

development, including computational modelling for predictive toxicity, computer-aided drug 

design, and clinical trial administration, make extensive use of digital health-focused technology in 

the pharmaceutical and related industries [6]. 

Machine learning in Drug Development (ML-DD):The field of pharmaceutical research and 



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

140 

 
https://internationalpubls.com 

development is poised for a paradigm shift due to the advent of ML, a groundbreaking technology. 

The simultaneous dissolution of prior limitations on the gathering and processing of massive 

amounts of data and the revolutionary advancements in computing power are contributing factors. At 

the same time, it has become very costly to bring new medications to market and to patients. In spite 

of these challenges, the pharmaceutical business is showing interest in AI and ML approaches for 

their predicted improvements in efficiency, predictive power, and automated nature. In the last 

fifteen to twenty years, ML methods have been increasingly used to the process of discovering new 

drugs by Kolluri, S. et al., [13]. 

Pharma based Deep Learning in Drud Development (P-DL-DD):It is anticipated to have a 

profound impact on research endeavours within the pharmaceutical and life sciences industries, 

particularly in the area of systematic analysis of massive collections of comparable but intricate data 

samples. Currently, deep learning is dominating expert-level domains, particularly in perception-

intensive fields where traditional machine learning has failed. Automated image analysis is a 

common example because of the large number of pictures generated in several fields, such as digital 

pathology and high-content screening by Siegismund, D. et al., [22]. By using deep learning, 

innovative applications may be developed in the fundamental areas of 'human intelligence' that have 

been previously identified.  

Digital Health in CNN (DH-CNN):CNN to keep track of pharmaceutical businesses' financial 

transactions including AI and D/ML or ANN in drug research and discovery processes, and to 

demonstrate how AI and CNN idea may optimise analytical technique conditions and dosage form 

formula. Applying linear models, such as design of experiments, was the first step in optimising 

analytical technique conditions and dosage form formulas; further steps included using non-linear 

models, such as AI and CNN. However, by combining linear and non-linear models in optimisation 

procedures, vital process parameters and material characteristics were able to be appropriately 

identified as having an impact on important quality attributes by Jariwala, N. et al., [15]. 

In summary, telemedicine, wearables, and machine learning are just a few examples of the digital 

health technologies that are changing the face of pharmaceutical research and healthcare delivery 

[16]. Computational modelling, DL and AI-driven medication design have all made strides towards 

more efficient data processing. These advancements in technology have the potential to revolutionise 

the pharmaceutical industry by enhancing the efficiency and effectiveness of drug development, 

clinical trials, and individualised treatment programs. 

3. PROPOSED METHOD 

This situation is true across all industries, including the pharmaceutical business. No one can deny 

the influence that AI has had on the pharmaceutical business. Artificial intelligence has already 

begun to be used by several pharmaceutical corporations throughout the globe. Due to a number of 

contributors, this state-of-the-art technology has come a long way in the pharmaceutical sector, and 

its future seems bright. All of this will be covered in today's article. However, before itgets into the 

topic of AI in pharma and its potential uses, effects, and future, let's take a look at the present state of 

the sector, whereby not all businesses are using AI. 

  



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

141 

 
https://internationalpubls.com 

 

Figure 1: AI in Pharma and Healthcare Industry 

AI allows for the rapid and efficient execution of repetitive processes, such as data input and the 

analysis of medical test results. Data administration in healthcare is now a breeze due to AI. Digital 

automation allows for the uniform and rapid collection, storage, reformatting, and tracing of all data. 

Artificial intelligence accurately assesses healthcare systems and aids healthcare practitioners in 

making optimal choices to maintain system organisation and improve patient care. It is possible to 

use digital methods for generating invoices. Electronic medical records provide healthcare providers 

with a wealth of data for analysis. In the time between patient visits to the doctor, digital or virtual 

nurses check in with them. The use of AI to reduce unnecessary doctor's visits is a huge step forward 

in healthcare technology. These applications will be most helpful for patients with chronic illnesses 

or those who have a hard time following their doctors' orders is shown in figure 1. 

𝑀𝑛𝐵(∀ − 𝑒𝑤 ′′) =  𝜕2𝑉(𝐸𝑤 − 𝑟𝑡′′) ∗ 𝐸𝑞(𝛿 − 𝑒𝑟 ′′)(1) 

In drug candidate assessments, the equation 1, 𝑀𝑛 represents the outcomes or metrics, while 

the𝐵(∀ − 𝑒𝑤 ′′) represents the larger framework of AI algorithms that are used. The interaction 

between trial parameters (𝜕2𝑉) and several AI-driven prediction models (𝐸𝑤 − 𝑟𝑡′′) is shown on 

the equation, highlighting 𝐸𝑞(𝛿 − 𝑒𝑟 ′′) these interactions improve the accuracy of clinical trial 

results and optimize the selection of medication candidates. 

 

Figure 2: The Process of Drug Discovery 



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

142 

 
https://internationalpubls.com 

In figure 2, a target is a biological molecule that, when exposed to a medicinal substance, has the 

desired effect, such as illness prevention. Cells, tissues, and animal models are used by scientists to 

determine whether a certain target is responsive to a drug. A hit is a tiny molecule that successfully 

binds to and affects a verified target. High throughput screening (HTS) is now the standard method 

for researchers to choose potential hits from a vast library of biological and chemical molecules. The 

hits that were chosen in the previous stage are tested in various ways to see whether they work or 

not. Here, researchers narrow down the pool of potential possibilities to a manageable amount, which 

they refer to as leads. The goal of this process is to make the lead molecules better in every way, 

including making them more effective, less poisonous, and easier to absorb. Repurposing lead 

compounds for use against various targets (diseases) is possible after further investigation. 

Researchers conduct in vivo assays on live creatures (animals) and in vitro assays on test tubes to 

choose drug candidates for clinical trials at the conclusion of drug development. Only one out of 

every twenty-five leads is really pursued further.  

(𝑀𝑦(𝜕2𝑅𝑓 − 𝑣𝑐 ′′)) = ∝ 𝑇(𝑌 − 𝑢𝑟 ′′) ∗  𝐸𝑦𝑡𝑤 ′′(2) 

The model's output about resource optimization is represented by equation 2𝑀𝑦, and the changes 

made to resource factors affecting drug development are denoted by 𝜕2𝑅𝑓 − 𝑣𝑐 ′′. Indicating the 

framework's ability to increase treatment personalization and overall drug development efficiency, 

Th shows how the proportionality constant 𝑌 − 𝑢𝑟 ′′interacts with patient-specific variables (𝐸𝑦𝑡𝑤 ′′) 

and efficacy metrics (∝ 𝑇) for analysis of efficiency. 

 

Figure 3: The Bock Diagram of Drug Development based on Artificial Intelligence 

Complete data gathering, including genetic data, chemical compound libraries, and clinical trial data, 

is the first step in the AI-driven drug development process. To speed up molecular discovery and 

interaction simulation, artificial intelligence technologies, such as machine learning models, are built 

on top of this massive information. These algorithms outperform more conventional approaches to 

identifying possible medication options. Pharmaceutical compound optimisation and clinical trial 



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

143 

 
https://internationalpubls.com 

outcome prediction will constitute the next stage. Artificial intelligence systems have place the use of 

data such as historical information on previous trials and patients to forecast the outcome success rate 

with better precision. Such attributes eliminate the headache of trial and error and help improve the 

drug development process in both time and cost. He further explains that with the help of artificial 

intelligence it is also possible to design new treatment combination for every patient, thus enhancing 

efficacy and lowering adverse effects. The ultimate product of this procedure, which meshes both 

endorsed drugs and specific management plans as depicted in figure 3, guarantees a paradigm shift in 

health care towards a more effective, expeditious and patient oriented facet. 

(𝐸𝑏 = 𝛿 ′ + 𝛼(∃ + 2𝑤𝑞′′)) − 𝐸𝑡𝑦(𝛽 + 𝑠𝑑′′)(3) 

The projected gains from AI-driven drug development procedures are represented by the equation 

3,𝐸𝑏, while baseline increases𝛽 + 𝑠𝑑′′ in drug effectiveness are accounted for by∃ + 2𝑤𝑞′′. The AI 

algorithms' enhancing benefits on drug candidates are captured by the term 𝛿 ′ + 𝛼, whereas the costs 

or risks related to resource expenditures are reflected by subtracting 𝐸𝑡𝑦 for analysis of cost-

effectiveness. 

To improve compound discovery and therefore shorten the periods for conducting clinical trials, the 

drug development approach powered by artificial intelligence uses big data such as genetic and 

clinical trial data. Offshore outsourcing of drug discovery and development services has witnessed 

the rise of artificial intelligence and machine learning applications that significantly reduce cost and 

duration by improving in the assessment of drug candidates and outcomes of the trials. With the 

growth of artificial intelligence, there is also the possibility of modifying and creating treatment 

programs to achieve better results with fewer side effects. The final product, which includes both 

marketed drugs and individualized therapy, creates a more efficient and patient oriented process of 

drug development and provision of health care services. 

4. RESULT AND DISCUSSION 

Regression models based on AI systems have revolutionized the pharmaceutical industry in the 

sphere of drug creation. There are some common issues with the standard approaches such as high 

costs, extended timelines, and low efficiency. To address these issues, the DD-AI framework 

employs the use of AI and ML algorithms to maximize the efficiency, precision, and cost of the drug 

discovery process. 

 
Figure 4: The Analysis of Efficiency 

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10

E
ff

ic
ie

n
c
y
 R

a
ti

o
(%

)

Number of Samples

P-BT-DH ML-DD P-DL-DD DH-CNN DD-AI



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

144 

 
https://internationalpubls.com 

The DD-AI platform streamlines and automates several crucial steps in the drug development 

process, greatly increasing efficiency using 2. Artificial intelligence systems can quickly sift through 

massive biological and chemical databases, speeding up the process of finding potential new 

medications. Clinical trials have historically had high attrition rates, but machine learning algorithms 

may anticipate trial success by evaluating factors like patient reactions and medication interactions. 

Also, by mimicking chemical interactions and honing candidate selection, AI shortens the drug 

development process and cuts expenses. Taken as a whole, these innovations simplify the 

development process, which speeds up and lowers the cost of producing safe, effective medications. 

The efficiency ratio is gained by 97.74% is shown in figure 4. 

 

Figure 5: The Analysis of Cost Effective 

By streamlining processes and making better use of available resources, the DD-AI framework 

significantly lowers the price tag on drug development using 3. Artificial intelligence-driven 

technologies shorten the traditional drug development procedures that rely on expensive and time-

consuming trial-and-error methods. AI lessens the burden of time-consuming and financially-

intensive research by early prediction of the most viable medication options. Additionally, lab labour 

and clinical trial expenses are reduced since AI-powered simulations take the role of actual testing. 

Using AI also helps in finding the right people to participate in trials, which helps keep costs down. 

With this precision-driven method, it can save a tonne of money without sacrificing quality in drug 

development since only go on with the most promising candidates. The cost-effective ratio is reduced 

by 25% is shown in figure 5. 

In summary, The DD-AI framework accelerates drug development, reducing costs and improving 

precision. By optimizing candidate selection, predicting trial outcomes, and simulating chemical 

interactions, AI fosters a faster, more efficient, and patient-centered approach. 

5. CONCLUSION 

There may be a correlation between the rise in the number of start-ups in the pharmaceutical industry 

and the fact that AI is constantly improving and offering new tools with the goal of making life 

easier for pharmaceutical businesses. This has implications for both the medication development 

process and the product's total lifespan. The rising expense of medications and treatments is just one 

of many complicated problems plaguing the healthcare system today; as a whole, society need 

0 20 40 60 80 100

1

2

3

4

5

6

7

8

9

10

Number of Samples

C
o

st
 E

ff
e
c
ti

v
e
 R

a
ti

o
(%

)

DD-AI DH-CNN P-DL-DD ML-DD P-BT-DH



Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

145 

 
https://internationalpubls.com 

substantial reforms in this area. Every patient’s requirement can be addressed effectively by utilizing 

AI in pharmaceutical product production, wherein personal pharmaceutical construction with proper 

dosage, release characteristics, and other components is feasible. The necessity for automation within 

the process will be further enhanced by cutting-edge technologies based on AI ensuring rapid 

product development and improved product quality, enhanced safety of the production facility and 

resources, and minimized expenses. Apart from aiding fast decision making and thus producing 

better products with the assurance of consistency in the batch-to-batch production of goods, Artificial 

Intelligence has a great potential in promoting the Strategy more and integrating it with the dosage 

forms of the medicine produced. The powerful use of AI in clinical research trials may include 

evaluation of safety and efficacy of the product. It can also assist in market analysis and forecasting 

to target effective market strategies and price effectively. 

Future Work: The paper of the DD-AI framework in future may explore how AI can be best utilized 

with newer areas such as better genomic and quantum computing advancements. Due to these 

enhancements, it is likely that the development of medications can be more accurate when more 

complex chemical interactions are being modeled and more assortment of databases are measured. 

There may also be better individual treatment programs due to more use of artificial intelligence (AI) 

in personalized medicine, especially real-time monitoring and predictive analytics. An additional key 

element to ally these regulatory challenges would be to create AI models respectful of needlessly 

high standards of safety and effectiveness and quickly gain approval for them. Further studies may 

delve into the issue of ethics and necessity for equity in the application of AI in treatment. 

REFERENCES 

[1] Mak, K. K., &Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future 

prospects. Drug discovery today, 24(3), 773-780. 

[2] Kodric, Z., Vrhovec, S., &Jelovcan, L. (2021). Securing edge-enabled smart healthcare systems with blockchain: A 

systematic literature review. Journal of Internet Services and Information Security, 11(4), 19-32. 

[3] Lamberti, M. J., Wilkinson, M., Donzanti, B. A., Wohlhieter, G. E., Parikh, S., Wilkins, R. G., & Getz, K. (2019). 

A study on the application and use of artificial intelligence to support drug development. Clinical 

therapeutics, 41(8), 1414-1426. 

[4] Lavanya, P., Subba, R.I.V., Selvakumar, V. & Shreesh V Deshpande. (2024). An Intelligent Health Surveillance 

System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa 

Communication and Internet of Medical Things (IoMT). Journal of Internet Services and Information Security, 

14(1), 165-179. 

[5] Borisa, P., Singh, D., & Rathore, K. S. (2020). Impact of artificial intelligence on pharma industry. Manipal Journal 

of Pharmaceutical Sciences, 6(1), 9. 

[6] Sofiene, M., Souhir, C., Yousef, A., & Abdulrahman, A. (2023). Blockchain Technology in Enhancing Health Care 

Ecosystem for Sustainable Development. Journal of Wireless Mobile Networks, Ubiquitous Computing, and 

Dependable Applications,14(3), 240-252. 

[7] Lou, B., & Wu, L. (2021). AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a 

large-scale examination of bio-pharma firms. Evidence from a Large-scale Examination of Bio-pharma 

Firms,(March 15, 2021). MISQ Forthcoming. 

[8] Malathi, K., Shruthi, S.N., Madhumitha, N., Sreelakshmi, S., Sathya, U., & Sangeetha, P.M. (2024). Medical Data 

Integration and Interoperability through Remote Monitoring of Healthcare Devices. Journal of Wireless Mobile 

Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 60-72. 

https://doi.org/10.58346/JOWUA.2024.I2.005 

https://jowua.com/article/2023.I3.018/71035/
https://jowua.com/article/2023.I3.018/71035/


Communications on Applied Nonlinear Analysis 

ISSN: 1074-133X 

Vol 32 No. 2s (2025) 

 

146 

 
https://internationalpubls.com 

[9] Yadav, S., Singh, A., Singhal, R., & Yadav, J. P. (2024). Revolutionizing drug discovery: The impact of artificial 

intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy. 

[10] Bobir, A.O., Askariy, M., Otabek, Y.Y., Nodir, R.K., Rakhima, A., Zukhra, Z.Y., Sherzod, A.A. (2024). Utilizing 

Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity. 

Natural and Engineering Sciences, 9(1), 72-83. 

[11] Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., & Sony, H. T. (2023). Artificial 

intelligence and machine learning technology driven modern drug discovery and development. International Journal 

of Molecular Sciences, 24(3), 2026. 

[12] Mumtaj Begum, H. (2022). Scientometric Analysis of the Research Paper Output on Artificial Intelligence: A 

Study. Indian Journal of Information Sources and Services, 12(1), 52–58. 

[13] Kolluri, S., Lin, J., Liu, R., Zhang, Y., & Zhang, W. (2022). Machine learning and artificial intelligence in 

pharmaceutical research and development: a review. The AAPS journal, 24, 1-10. 

[14] S. Neelima, Manoj Govindaraj, Dr.K. Subramani, Ahmed ALkhayyat, & Dr. Chippy Mohan. (2024). Factors 

Influencing Data Utilization and Performance of Health Management Information Systems: A Case Study. Indian 

Journal of Information Sources and Services, 14(2), 146–152. https://doi.org/10.51983/ijiss-2024.14.2.21 

[15] Naidu, TM Praneeth, Chandra Sekhar, and Pradeep Kumar Boya. "Low Power System on Chip Implementation of 

Adaptive Intra Frame and Hierarchical Motion Estimation in H. 265." Journal of VLSI Circuits and Systems 6.2 

(2024): 40-52. 

[16] Veera Boopathy, E., Peer Mohamed Appa, M.A.Y., Pragadeswaran, S., Karthick Raja, D., Gowtham, M., Kishore, 

R., Vimalraj, P., &Vissnuvardhan, K. (2024). A Data Driven Approach through IOMT based Patient Healthcare 

Monitoring System. Archives for Technical Sciences, 2(31), 9-15. 

[17] Rai, H. T., G. W. Mu, and R. Q. Lu. "Integrated Dielectric Resonator Antenna to Traditional Antenna for Better 

Bandwidth and Gain." National Journal of Antennas and Propagation 6.2 (2024): 9-16. 

[18] Antoniewicz, Baotic, and Silva Dreyfus. "Techniques on Controlling Bandwidth and Energy Consumption for 5G 

and 6G Wireless Communication Systems." International Journal of communication and computer 

Technologies 12.2 (2024): 11-20. 

[19] Kulkov, I. (2021). The role of artificial intelligence in business transformation: A case of pharmaceutical 

companies. Technology in Society, 66, 101629. 

[20] Uvarajan, K. P. "Integration of Artificial Intelligence in Electronics: Enhancing Smart Devices and 

Systems." Progress in Electronics and Communication Engineering 1.1 (2024): 7-12. 

[21] Kasoju, N., Remya, N. S., Sasi, R., Sujesh, S., Soman, B., Kesavadas, C., & Behari, S. (2023). Digital health: 

trends, opportunities and challenges in medical devices, pharma and bio-technology. CSI Transactions on ICT, 

11(1), 11-30. 

[22] Siegismund, D., Tolkachev, V., Heyse, S., Sick, B., Duerr, O., & Steigele, S. (2018). Developing deep learning 

applications for life science and pharma industry. Drug research, 68(06), 305-310. 

[23] Narayanan, R. R., Durga, N., & Nagalakshmi, S. (2022). Impact of artificial intelligence (AI) on drug discovery and 

product development. Indian Journal of Pharmaceutical Education and Research, 56(3), S387-S397. 

https://doi.org/10.51983/ijiss-2024.14.2.21