INTRODUCTION:

1.1. Personalized Medicine:

The manual processes and human knowledge that make up the majority of the traditional pharmacy system can lead to errors, delays, and inefficiencies. For example, reading the prescription, delivering the medication, and verifying the frequency and amount are all manual processes involved in completing a prescription. These manual processes are prone to errors and can be quite time-consuming. Furthermore, the traditional pharmacy system's incapacity to tailor prescription regimens for individual patient counseling may limit a drug's effectiveness. However, by using AI-powered solutions, pharmacies can enhance operations and circumvent these limitations. AI can assist in automating pharmacy workflow activities, such as prescription interpretation and medicine delivery, which can reduce errors and boost efficiency. By analyzing enormous amounts of patient data, AI may also assist pharmacists in developing personalized medication regimes that are based on each patient's unique requirements and medical history.¹

Despite its success in providing patients with the medications they require; the traditional pharmacy system's use is limited by its manual processes and lack of flexibility. AI-powered solutions can be used to make the pharmacy system more precise, effective, and customized in order to get around these limitations and enhance patient outcomes. AI in pharmacy apps provides users several advantages and has significant ramifications for people at home. Artificial Intelligence in Pharmacy. Although there are a number of research gaps that need to be filled, as seen in Fig. 1, the application of AI in pharmacy issues shows promise. Patient satisfaction with the technology, long-term effects like how it affects medication adherence, ethical considerations like data privacy and potential biases, technical difficulties like data integration and system upkeep, and patient and pharmacist usability are some of these.²

Researchers can better grasp the ramifications of implementing AI in the pharmacy system, create solutions that optimize its advantages, and lessen its drawbacks while guaranteeing ethical use by filling up these research gaps. A study on the application of AI in the pharmacy system is novel since it has the potential to completely transform the sector. Although research on AI's application in healthcare has been done before, this cutting-edge technology may provide responses to natural language inputs that are human-like, enabling patients to have conversational interactions with it. It can assist in recognizing the special difficulties and possibilities brought about by this technology, as well as its drawbacks and moral dilemmas. Researchers can create solutions that optimize AI's advantages while minimizing its drawbacks and guaranteeing its ethical application by comprehending these difficulties.³

1.2. Need for AI in pharmacy systems:

It's crucial to remember that, even while there is increasing interest in integrating AI into pharmacy systems, its deployment requires careful thought and planning.¹ AI has the ability to transform the sector and offer patients 24/7 care and customized medications, but it must be utilized responsibly and any possible drawbacks or problems must be addressed.² In order to guarantee the efficient and responsible application of AI in pharmacy systems, it is founded on a careful and useful data strategy.⁴

The use of AI in pharmacy systems will help satisfy the increasing demand for health services, which will benefit the expanding population. AI helps patients better manage their medicine, which reduces the need for frequent visits to health service providers.

This is made possible by 24/7 support and individualized pharmaceutical therapy. This facilitates patient access and increases the strain on the healthcare system. AI may also lower medication and needless drug interactions, enhance patient outcomes, and lower medical expenses.

Growing populations and rising health service demand can be addressed using AI that enhances medication administration and lessens the effort for healthcare personnel. AI integration into pharmaceutical systems can improve people's lives in a number of ways. Initially, this facilitates better access to medical care and offers patients individualized prescription drugs and round-the-clock assistance.⁵

This lessens the need for frequent trips to the doctor and helps people better manage their medicine. This is particularly helpful in places with limited or inaccessible medical care. Second, AI has the potential to save medical expenses by minimizing prescription errors and the need for preliminary secondary medications, which could lead to shorter hospital stays and lower medical expenses. Healthcare providers' workloads can be lessened by AI. This enables you to concentrate on more difficult assignments and raises the standard of service you can provide. Generally speaking, integrating AI into pharmacy systems can raise access to healthcare services, lower healthcare expenditures, and improve healthcare outcomes.⁶

1.3 A.I. application in personalized medicine:

Beyond conventional models in a single size, personalized (precise) medicine seeks to modify genomes, physiology, environment and lifestyle prevention, diagnosis, and therapy. Due to its expertise in drug discovery, clinical development, and marketing, the pharmaceutical sector is uniquely positioned to carry out this ambition. The finest patient stratification and customized treatment plans benefit from all of this. IT solutions for collecting high data sources (electronic medical cards, visualization, multi-friends, portable and actual proofs) and automating judgments at scale are part of artificial intelligence (AI), which includes machine learning (ML), deep learning (DL), generic models, and training (RL).[11] There are several reasons why the pharmaceutical industry is adopting artificial intelligence. Choose patients who have a higher chance of responding to experimental treatment. Optimize dose selection and safety monitoring. Through decision help and remote monitoring, it also enhances patient commitment and results. However, data management, future verification, explanation, and regulatory compatibility are all necessary for successful translation in addition to algorithm efficacy. With a focus on methodologies, applications, evidence, management, and future potential, this paper examines the integrated use of AI in customized medicine and patient care at pharmaceutical institutions using real-world data.⁸ The rise of customized medicine demonstrates a change in the paradigm of healthcare, from a standard.

"Size" approach to everything to tailoring treatment plans to each patient's particular needs. This idea is founded on the knowledge that disease risk, medication response, and treatment outcomes are greatly impacted by medicine diversity in genetic composition, disease biology, environmental influences, and lifestyle.The pharmaceutical industry, which serves as the foundation for contemporary medication research, is introducing more and more instruments that can take advantage of these variations to boost productivity, lessen adverse effects, and eventually produce safer, more effective, and more stable treatments.⁹ One of the most important reasons contributing to this shift in healthcare accuracy is artificial intelligence (AI). AI describes computer programs that can create models, make predictions, and get better over time using a range of datasets. Artificial intelligence is being used throughout the whole drug development lifecycle in the pharmaceutical industry. enhancing post-market pharmakon monitoring, clinical trial design, regulatory submission, and drug detection. Effective ideas that were previously unattainable with rule-based statistics or conventional rules can be produced with the help of extensive, diverse data sources, including genomic and proteomic profiles, electronic medical records (DSE), visualization data, laptops, and real AI data. Numerous cutting-edge prospects arise from the incorporation of AI into personalized medicine. MultiMate can be used by automated learning algorithms to assess data in order to forecast which patient subgroups are most likely to respond to a given intervention and to identify new treatment targets. When applied to molecular data and visualization, detailed learning models can improve disease categorization and speed up and improve diagnosis. AI-generation techniques can duplicate current compounds with uncommon genetic subtypes and create new therapeutic molecules. Individual dose techniques have been optimized by training and physiological hybrid training based on.¹⁰

Pharmacokinetic/ pharmacodynamic models (PBPK/ PKPD). Additionally, compared to the potential of transforming treatment scenarios into real interventions, the rise of "digital twins"—a patient air replica built from combined biological and clinical data—allows for a reconsideration of clinical judgments and patient safety.

2. AI Methodologies Driving Personalized Medicine:

Instead of using "one-size-fits-all" guidelines for healthcare, personalized medicine bases interventions on the unique characteristics of each patient. Statistical machine learning and artificial intelligence (AI) are two powerful techniques that are required to interpret and create prognostic models from underlying data as epidemiological datasets continue to grow in size and complexity. Such analysis demonstrates how machine learning's accurate predictions can support personalized medicine. Furthermore, additional artificial intelligence (AI) tools like computer vision and natural language processing can be very helpful in individualized treatment for patients with spine disorders.¹¹ In this report, we go over the recent advancements in using AI in research on spine conditions, particularly degenerative spine disease and traumatic spinal cord injury. Studies employing AI to create precise prognostic models and extract crucial data are described.

2.1. Important Specifications for Personalized Medicine:

Prior to going over particular instances, we would like to suggest qualities of a "good" machine learning model for personalized medicine.First and foremost, ML prediction needs to perform well, as evidenced by the model's ability to accurately forecast the result.Since a learning tool essentially creates a model, there is a trade-off between test error when we add new data to our model and training error when we are building the model.By gathering more training data, we can create a complex model with a low training error. Nevertheless, this model will exhibit poor generation in the test data, phenomenon referred to as model overfitting. Therefore, we should create a model that strikes a good balance between being too complicated and not too simple.¹²

2.2. A.I Methodologies Relevant to Personalized Medicine:

Classical Machine Learning in Personalized Medicine and Healthcare:

Artificial intelligence in the pharmaceutical and health sectors is based on classical machine learning (ML). Its interpretability, robustness, and regulatory compliance make it popular, and it incorporates algorithms that learn from data to identify patients or forecast outcomes [13]. The primary families of classical machine learning (ML)— decision trees, support vector machines (SVM), regression models, and ensemble methods—will be covered in more detail below, along with connections and applications.^15,16,17

3.A.I Applications in Personalized Medicine:

1. Genomic Medicine:

Introduction:

The field of medicine known as genomic medicine makes use of a person's genetic information to inform disease diagnosis, prognosis, prevention, and treatment. Largescale genome analysis is now feasible thanks to the Human Genome Project's completion in 2003 and the advancement of next-generation sequencing (NGS) technologies.

2. Drug Development and Target Identification:

Pharma firms can find new drug targets by connecting genes to disease pathways thanks to genomic insights.

As an illustration, PCSK9 inhibitors for hypercholesterolemia were created following the discovery of mutations linked to the regulation of cholesterol 3

3. Clinical Trial Designs: Patients are increasingly being stratified in trials according to genetic signatures and biomarkers. For instance, patients are recruited for basket trials and umbrella trials based on genomic changes rather than just the location of the tumor.¹⁸

4. Application of AI for Targeted Treatment:

4.1. Pharmacogenomics:

In order to comprehend how patients metabolize and react to medications, pharmacogenomics uses genetic knowledge. This field is improved by artificial intelligence (AI) models, which predict drug response patterns by analyzing high-dimensional genomic and clinical data. Key metabolic enzymes that affect drug metabolism, like cytochrome P450, TPMT, or VKORC1, can have their variations modeled by AI algorithms. This aids in the prediction of negative drug reactions and the optimization of dosage schedules for safer treatment. AI additionally makes drug repurposing easier by combining chemical, pharmacological, and genomic data to find novel therapeutic applications for already-approved medications. The development of companion diagnostics, which pinpoint patients most likely to benefit from particular therapies, is also aided by AI.

4.2. Oncology:

When it comes to using AI in personalized medicine, oncology has been at the forefront. Because cancer is so diverse, choosing an effective treatment requires integrating pathology, imaging, and genomics. In order to suggest precision treatments, AI-enabled systems can analyze genomic data in addition to radiomic and histopathology data. Machine learning algorithms, for instance, are being used more and more to forecast which patients will react to immunotherapies like checkpoint inhibitors. Additionally, AI facilitates the identification of molecular targets via extensive genomic analysis, which results in the development of precision oncology medications and actionable biomarkers. Predicting treatment response and progression is another crucial application that enables physicians to dynamically modify therapies to optimize benefit and reduce needless toxicity.¹⁹

4.3. Chronic Disease Management:

The majority of the world's health problems are chronic, like diabetes and heart disease. Through the prediction of disease progression and the recommendation of tailored interventions, AI makes a proactive care model possible. Predicting complications in diabetes is possible with AI models trained on genomics, wearable sensor data, and longitudinal electronic health records (EHRs).3 cardiovascular risk (e.g., neuropathy, retinopathy. heart failure, atrial fibrillation. Combining lifestyle factors, biomarkers, and clinical data results in personalized interventions. Continuous glucose monitors (CGM) powered by AI, for instance, can predict episodes of hypoglycemia, and AI-based ECG tools can identify early atrial fibrillation risk¹⁹.

5. AI Applications in Patient Care:

5.1. Clinical Decision Support Systems (CDSS):

One of the most obvious uses of AI in healthcare is in clinical decision support systems, or CDSS. They give physicians data-driven, evidence-based advice to help them make better decisions. To suggest the best diagnostic procedures, drugs, or treatment plans, AI-enabled CDSS systems examine genetic profiles, imaging, lab data, and electronic health records (EHRs). AI, for instance, can lower the risk of medical errors by providing real-time alerts about possible drug interactions, contraindications, or adverse reactions. Additionally, patients at high risk of clinical deterioration can be identified by predictive algorithms, allowing critical care units to intervene early.

5.2. Remote Patient Monitoring & Telehealth:

Wearable technology, sensors, and the Internet of Things are used by AI-powered remote patient monitoring (RPM) systems to continuously gather patient vitals like blood pressure, heart rate, oxygen saturation, and glucose levels. AI algorithms are used to analyze these data streams in order to identify irregularities, forecast issues, and instantly notify medical professionals. Elderly patients or those living in remote areas can receive high-quality care at home thanks to this proactive care approach, which also improves chronic disease management and lowers hospital readmissions. By prioritizing urgent cases and helping doctors with virtual consultations, AI-enhanced telehealth platforms also enhance triaging, increasing the accessibility and scalability of healthcare.

5.3. Virtual Health Assistants and Chatbots:

Virtual assistants and chatbots powered by AI provide patients with round-the-clock, individualized health support. They are able to set up appointments, remind patients to take their medications, and provide pre- and post-operative care instructions. Conversational interactions are made possible by Natural Language Processing (NLP), which makes chatbots accessible and easy to use. In the context of chronic care, they help patients monitor lifestyle factors like sleep, food, and exercise and enhance adherence to medication schedules. In addition to empowering patients, these tools lighten the workload for clinicians.²¹

5.4. Mental Health and Behavioral Predictions:

One crucial area where AI is revolutionizing practice is mental health. AI systems can identify subtle behavioral cues that indicate depression, anxiety, or relapse risk by analyzing speech, text messages, smartphone usage patterns, and wearable data. In patients with psychiatric disorders, for instance, AI monitoring of sleep and activity patterns can predict early relapse, while voice modulation and linguistic markers in text can predict depressive episodes. In the end, these tools lessen stigma and increase access to mental health care by promoting early intervention, remote counseling, and tailored therapy modifications.²²

5.5. Benefits of AI in Personalized Medicine and Care:²²

Precision: To customize treatment plans, AI examines genomic, clinical, and lifestyle data. In order to increase effectiveness and reduce side effects, pharmacogenomics -based AI models forecast how patients will metabolize medications.

Efficiency: AI lessens the need for random prescriptions. Clinical Decision Support Systems (CDSS) help doctors make better decisions about diagnosis and treatment in real time.

Early Detection: Before symptoms show up, machine learning models identify patterns of disease. Deep learning can detect cancerous lesions and diabetic retinopathy earlier than human specialists, for example.

Patient Empowerment: Wearables and chatbots driven by AI offer personalized recommendations, reminders, and real-time insights, which boost engagement and adherence.

Cost Reduction: AI reduces hospitalization rates and healthcare expenses by preventing ineffective treatments, minimizing adverse events, and forecasting the course of diseases.

6. Challenges and Limitations:

While there are many potential advantages to using artificial intelligence (AI) in personalized medicine and healthcare, there are also important issues that need to be resolved for its safe, efficient, and moral application.

7. Future Directions of AI in Personalized Medicine and Patient Care:

AI is quickly progressing in the healthcare industry from assisting instruments to revolutionary enablers. The following new developments demonstrate how AI has the ability to transform pharmaceutical innovation and patient care

7.1. Federated Learning:²³

Data privacy is one of the main obstacles to the adoption of AI. Federated learning provides an answer by facilitating cooperative AI model training amongst various institutions without necessitating the exchange of raw data. While AI models learn collectively from distributed datasets, hospitals can store sensitive patient data locally. This strategy guarantees adherence to data protection laws like GDPR and HIPAA, preserves privacy, and lowers security threats.

7.2. Multi-omics Integration:

Combining the various biological layers of proteomics, metabolomics, transcriptomics, and genomics is necessary for modern precision medicine. When it comes to combining these massive datasets, AI is excellent at revealing hidden correlations and enhancing disease stratification. Machine learning models that combine multi-omics data, for instance, have been successful in predicting treatment outcomes and cancer subtypes. ²⁵

8. CONCLUSION:

In the pharmaceutical sector, artificial intelligence (AI) is quickly changing personalized treatme nt and patient care.AI has made it possible to achieve previously unheard- of levels of drug discovery, development, repurposing, and optimization by combining cuttingedge approaches including machine learning, deep learning, reinforcement learning, natural lang uage processing, and federated learning.

Its uses range from clinical decision support systems and precision dosage to virtual health assist ants, mental health prediction, and remote monitoring, all of which increase productivity, accuracy, and patient involvement.

More precise interventions catered to a person's genetic, clinical, and lifestyle profile are made p cossible by the incorporation of AI into personalized medicine.

AI can enhance therapeutic efficacy, minimize side effects, and maximize the use of healthcare re sources, as shown by pharmacogenomics, cancer, chronic disease management, and multiomics data integration. The practical advantages and increasing uptake of AIdriven approaches in healthcare are demonstrated by real-world applications like IBM Watson for oncology, DeepMind for retinal disorders, and AIpowered chatbots from Ada Health and Babylon Health.

Ensuring data privacy, reducing algorithmic bias, integrating with healthcare systems seamlessly, enhancing model openness, and negotiating regulatory frameworks are still difficult tasks, never the less.

For AI to be used in personalized medicine in a way that is safe, moral, and efficient, these restri ctions must be addressed. Future developments in multi-omics integration, federated learning, and explainable AI hold promise for improving patientcentered care even more, accelerating pharmaceutical innovation, and cutting down on the time a nd expense of drug development. AI has the ability to completely transform healthcare, providing genuinely customized treatment plans and better health outcomes globally, if ethical, legal, and technological factors are carefully taken into account.

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Received on 05.12.2025 Revised on 24.12.2025

Accepted on 10.01.2026 Published on 30.01.2026

Available online from February 05, 2026

Res. J. Pharma. Dosage Forms and Tech.2026; 18(1):77-82.

DOI: 10.52711/0975-4377.2026.00013

This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License. Creative Commons License.