Artificial intelligence (AI) is a science that has been studied for over 50 years. John McCarthy invented the term in 1956 to describe the idea that computers could someday learn to execute tasks through pattern recognition with little to no human intervention. In computer science, AI studies “intelligent agents,” or systems that “perceive their environment and take actions to maximize their chances of achieving some goal.” Siri on Apple's iPhone, Netflix, Alexa on Amazon Echo, and other well-known examples are all made possible by AI [1, 2]. Artificial intelligence was first coined in the 1950s as a naive concept that computers could display human intellect. In the early 1960s, the first expert system, known as 'DENDRAL,' mechanized organic chemists' decision-making and problem-solving behavior with two main programs, Heuristic Dendral and MetaDendral. Artificial intelligence (AI) sought a place in healthcare and pharmaceuticals in the 1960s and 1970s [2].

Although progress is slow, some examples of where AI in health care include work done at UC Health in Colorado, where an AI-based scheduling tool is used to optimize surgical schedules, and many reports of incorrect medication administration or even surgery at the wrong site can be tracked down and followed up on, something that computers are particularly adapted [3]. People and scientists speculate that AI will be the herald of humanity's demise, ushering in a dystopian worldview in which robots rule the planet. On the other side, many people believe that wiser decision-making will lead to a massive boost in job prospects and economic growth [4]. In this article, we will describe both the potential and future of AI in the healthcare and pharmaceutical industry.

Artificial intelligence is a large area of study that includes a variety of technologies that function together. The following are some of the most critical technologies in healthcare: Machine learning, Neural networks, and deep learning [5]. ML is one of the most frequent types of AI. It is an analytical technique for fitting models in data or learning by training models using data, according to a 2018 Deloitte poll of 1,100 US managers whose companies were already exploring AI. It is a broad technique based on several AI approaches [5].

Traditional machine learning can be commonly utilized in precision medicine in the healthcare and pharmaceutical industries, forecasting which treatment protocols are likely to succeed on a patient based on numerous patient attributes and treatments. Most machine learning and precision medicine applications require supervised learning, which requires a training dataset with a predetermined outcome variable (e.g., illness onset) [6].

Deep learning is the most sophisticated kind of machine learning, which uses neural network models with multiple layers of variables to predict outcomes. The term “deep” refers to machine learning's multilayered structure. The CNNs (Convolutional neural networks) are the most promising DL technology in image recognition [10]. Deep learning in healthcare is frequently used to image potentially cancerous lesions in radiography. In the case of cancer, deep learning enables more precise imaging than prior generations of computer-aided detection (CAD) image analysis approaches [11]. Deep learning is gaining popularity in radionics (a branch of medicine that uses data characterization algorithms to extract a large number of quantitative information from medical images) or the discovery of clinically relevant data that the naked eye cannot discriminate. Thousands of hidden and unidentified data points may exist in such models that have yet to be identified [12] (Fig. 2).

Fig. (1). Illustration of neural network.

Deep neural networks use a vast database of up to 192,284 drug-drug interactions to forecast drug-drug interactions and drug-food interactions for dietary recommendations, prescriptions, and novel compounds [13]. When it comes to individual safety, Daunhawer et al. employed machine learning to personalize safety in infants with hyperbilirubinemia [14]. Gaweda et al. also employed reinforcement learning to personalize pharmaceutical anemia therapy [15]. ML models recommend dose adjustments in real-time and could be extremely useful in customized healthcare [16].

ML has also demonstrated its value in bridging the gap between medication discovery and clinical development. For example, Hammann et al. used a decision tree method to predict the risk of Torsedes de Pointes from in vitro data, and Lancaster and Sobie used SVMs (Support Vector Machine Algorithm) to predict the risk of Torsedes de Pointes from in vitro data [17]. In a recent study, the ML-type control algorithm was combined with pharmacometricians' PK/PD models, resulting in a closed-loop control system [18].

AI is gaining traction in various fields, including the pharmaceutical business. In this review, the use of AI in various pharmaceutical industry sectors is highlighted, including drug repurposing, drug discovery and development process, improving pharmaceutical productivity, and clinical trials; such implementation reduces human workload while also achieving targets in a short amount of time. AI in the pharmaceutical sector has enabled the industry to develop safe and effective treatments for previously untreatable ailments [19]. AI algorithms can drastically cut the cost and time it takes to discover and develop new drugs. AI has proven its value in pharmacovigilance by assisting in creating drug and biological toxicity databases [20]. Deep learning greatly influences science and technology in general, as well as drug development.

AI-driven technologies can be employed for drug repurposing in the case of neglected tropical diseases, which represent significant problems in terms of disease burden and death rate in underdeveloped and developing countries. These computational methods are rapid, making them excellent for screening huge libraries of available chemicals against specific molecular targets or disorders and repurposing current medications, clinical trials, and approved natural products [21]. Repurposing is advantageous since any leads discovered have already been evaluated for safety in people, allowing them to be tested in humans more rapidly and affordably than completely new medications [22].

AI has boosted the possibilities of leveraging huge data in pharmaceutical sciences, particularly machine learning technologies that allow computers to “learn” and perform jobs. The use of several computational approaches, such as molecular dynamics simulations and machine learning techniques, to forecast the water solubility of medicinal compounds has gained attention [23]. During the pre-formulation stage of the drug development process, the physicochemical characteristics of the drug material are assessed [24]. The stability, interaction with excipients, solubility, and bioavailability of a pharmaceutical substance are all influenced by its physicochemical properties. Transfer learning has shown to be a potential machine learning approach for further investigation in pre-formulation studies [25].

Fig. (2). Components of deep learning algorithm.

Artificial intelligence applications in drug discovery and design have significantly benefited from deep learning. In the United States, the Precision Medication Initiative was established to provide access to personalized medicine for the treatment of chronic disease conditions, such as cancer and diabetes [26]. SBDD (Structure-Based Drug Design) has proven instrumental in the rapid advancement of precision medicine. Computational modeling and simulation methods have been widely employed to develop new biomarkers, forecast potential intercellular pathways, and analyze protein conformational changes and cell activity [27]. Molecular dynamics simulation is a prominent computational tool for characterizing flexible binding sites and routes, kinetics, and thermodynamics, as well as understanding macromolecular conformational changes and their biological function, which might signal potential pathogenic processes [28] (Fig. 3).

Possible benefits of AI are described in Table 1 [68-75].

The healthcare system, like water, should adjust to changes in space and technology. It should not be stopped; instead, it should educate itself via its own experiences and strive to make ongoing advances in practice [87]. Today's age has progressed to the point where it is the most significant issue to alter and correct all potential flaws. Healthcare “systems thinking” considers the traditional interactions between patients and physicians and larger-scale organizations and cycles [88] (Fig. 5).

Fig. (5). Potential applications of AI in healthcare research.

Before using AI systems in healthcare, they must be “trained” using data generated from clinical activities, such as clinical laboratory, treatment, assignment medical notes, screening, electronic recordings from medical devices, physical examinations, diagnosis and images, etc., so that they can learn about similar groupings of subjects, subject-feature connections, and desired results [89, 90]. AI is successful when it contains a machine learning component for managing structured data and a natural language processing component for unstructured mining text. The implementation of AI in real life continues to face challenges. The first stumbling point is the limitations [91]. Under existing legislation, there are no standards to assess AI systems' safety and efficacy. Data exchange is a second stumbling block [92]. Apart from ethical problems, the main roadblock is that this novel feature of medical care necessitates a standardized, comparative assessment of the impact of robotic systems on health indicators, as well as assessments of changes in psychological and physical condition, side effects, and outcomes.

The pharmaceutical sector faces various complex difficulties, including increasing medication and therapy prices, and society demands considerable changes in this area. With the application of AI in pharmaceutical product manufacture, personalized medications with the requisite dosage, release characteristics, and other relevant factors may be considered according to specific patient demands. Using the maximum up-to-date AI based technology will now no longer simply shorten the time it takes for merchandise to attain the market, but it is going to additionally enhance product quality and safety of common production procedures, in addition to higher useful resource utilization and cost-effectiveness, emphasizing the significance of automation. We believe that AI has a bright future in healthcare and will favor medical practice.

AI can be useful in every step of drug development. ML and DL algorithms can bring about a shift in the paradigm of various intricacies of healthcare. AI in healthcare can potentially improve preventative care and overall quality of life. It can result in more precise treatment plans and, as a result, improved patient results. In clinical trials, AI-powered technologies may help determine the safety and efficacy of a medicinal product, as well as the best market positioning and pricing. Strict laws for the standardization of such data-driven advances are required to ensure the privacy and security of patient data. With the introduction of AI into the sector, there is a growing risk that numerous present employments may be lost. Hence, it should be implemented to reduce such losses and strain on healthcare providers. Shortly, AI is anticipated to become a valuable tool in the healthcare and pharmaceutical industries.

All authors contributed to the study's conception and design. Material preparation and data collection were performed by Madhana Kumar S, Narmatha S, Lakshmi Chandran, and Ankul Singh S. The first draft of the manuscript was written by Narmatha S, and all authors commented on previous versions. The analysis of the article was performed by Ankul Singh S. All authors read and approved the final manuscript.