Ranjan S, Singh A, Yadav R. Artificial Intelligence in Precision Medicine and Patient-Specific Drug Design. Biomed Pharmacol J 2025;18(March Spl Edition).

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Manuscript received on :16-12-2024
Manuscript accepted on :12-02-2025
Published online on: 26-02-2025

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Plagiarism Check: Yes
Reviewed by: Dr. Harish M
Second Review by: Dr. Rajendran Susai
Final Approval by: Dr. Prabhishek Singh

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Sweksha Ranjan, Arpita Singh and Ruchi Yadav*

Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, UP, India

Corresponding Author E-mail: ryadav@lko.amity.edu

DOI : https://dx.doi.org/10.13005/bpj/3088

Abstract

Artificial intelligence (AI) has emerged as a transformative force in personalized healthcare and precision medicine over the past decade. AI techniques like machine learning, deep learning, and natural language processing make possible the study of huge quantities of heterogeneous patient records from electronic health records, genomic profiles, wearable devices, and clinical trials. This allows for more accurate disease prediction, personalized treatment planning, and tailored drug discovery. Key areas of impact include AI-driven biomarker discovery, virtual drug screening, de novo drug design, and pharmacogenomics. The integration of AI is revolutionizing multiple aspects of precision medicine, from identifying novel therapeutic targets to optimizing clinical trial design and drug dosing. AI algorithms can detect subtle patterns in complex biological data, predict drug-target interactions, and simulate molecular behaviour to accelerate the typically costly and time-consuming drug development process. However, challenges remain around data quality, privacy, algorithmic bias, and equitable implementation. Ethical considerations regarding genetic discrimination and informed consent also need to be carefully addressed. This review examines the current applications, challenges, and future directions of AI in advancing patient-specific therapies and drug development.

Keywords

Artificial Intelligence; Deep Learning; Machine Learning; Pharmacogenomics; Precision Medicine

Introduction

Over the past ten years, the widespread implementation of artificial intelligence (AI) has been observed across all major sectors, including healthcare and medicine. Improvements in computational power, advances in algorithms, and the availability of large datasets have all contributed to this growth. AI is anticipated to contribute significantly to the realization of precision medicine, particularly in three key areas: (i) preventing diseases, (ii) tailoring diagnostic approaches, and (iii) customizing treatment strategies. ¹ A current account from the NAM (National Academy of Medicine) discussing the existing and forthcoming of artificial intelligence (AI) in healthcare emphasized “unprecedented opportunities” for enhancing specialist care and addressing human limitations like fatigue and inattention, as well as mitigating machine errors. During the last ten years, artificial intelligence has experienced substantial progress and acceptance across various fields, particularly amongst healthcare experts. AI offers abundant possibilities for developing intelligent products, establishing innovative services, and creating new business models. However, the implementation of AI also brings about social and ethical challenges related to security, privacy, and human rights concerns. ² The name “artificial intelligence” was initially introduced at the Dartmouth Summer Workshop in 1956, where it was largely described as “thinking machines.” AI is a comprehensive term that includes (and is occasionally used interchangeably with) machine learning and deep learning. Generally speaking, machine learning is considered a subset of artificial intelligence, while DP is a specialized area within machine learning that concentrates on complex artificial neural networks. ³

These innovations, including genomics, biotechnology, wearable sensors, and artificial intelligence (AI), are steering progress in three key directions. They are (i) transforming patients into the focal point of care; (ii) generating vast quantities of data that necessitate advanced analytical techniques; and (iii) establishing the groundwork for precision medicine. Nations are now showcasing their technological prowess through the computational might of supercomputers. In fields such as cardiology, dermatology, and oncology, deep learning algorithms have demonstrated diagnostic capabilities on par with, or surpassing, those of human physicians. AI development is broadly categorized into three phases: artificial narrow intelligence (ANI), artificial general intelligence, and artificial super intelligence. Several companies have already illustrated how supercomputers, deep learning, and ANI can bolster precision medicine efforts. ⁴ Utilizing high-performance computing (HPC) and artificial intelligence (AI) enables more precise risk assessment by analyzing complex clinical and biological datasets. This AI-driven approach to precision medicine allows healthcare providers to customize early interventions for each patient’s unique needs. ⁵

Figure 1: Application of AI in precision medicine   Click here to view Figure

Role of AI in Personalized Healthcare: An Overview

In today’s world, individualized medical care has become a crucial strategy for enhancing patient outcomes and satisfaction. Advances in medical technology and science now enable early disease detection and treatment, increasing the likelihood of successful recovery. This progress has also facilitated the creation of innovative therapies and treatments, resulting in improved patient outcomes. Artificial intelligence, utilizing machine learning and deep learning algorithms, can examine extensive patient datasets to deliver tailored healthcare services based on individual requirements. This approach has the potential to enhance patient results, decrease healthcare expenses, and improve overall care quality. AI can assist medical professionals in making more informed treatment decisions, tracking patient progress, and identifying potential health concerns. ⁶

The field of precision health informatics involves leveraging sophisticated data analysis and artificial intelligence (AI) methods to comprehend, forecast, and enhance health outcomes for individuals and populations. This discipline encompasses the gathering, maintenance, examination, and interpretation of varied datasets, including clinical, genetic, environmental, and lifestyle information, to guide tailored healthcare strategies. AI, particularly machine learning and deep learning algorithms, is instrumental in processing large-scale data to uncover valuable insights. These computational approaches can detect patterns, trends, and associations within data that might elude human analysts. Additionally, AI facilitates predictive modeling, which can assist in the early identification of diseases, customization of treatment plans, and projection of health outcomes. ⁷ Machine learning algorithms are employed in predictive models for disease progression and patient risk stratification to examine patients’ medical records, genetic data, and other relevant information. These models aim to forecast the likelihood of individuals developing specific diseases or the advancement of existing conditions. By identifying patients at risk, healthcare providers can implement preventive strategies to mitigate potential health issues. Additionally, these models assist in projecting the course of current illnesses, enabling healthcare professionals to modify treatment plans as needed. The development of predictive models involves training them on extensive patient datasets using machine learning techniques. These datasets encompass medical histories, genetic information, and other pertinent data. The algorithms analyze this information to detect patterns and correlations that can be utilized to estimate the probability of patients developing particular diseases. Deep learning models can determine the most effective reminder strategy for each patient by analyzing various patient data, including demographics, medical history, and previous responses to appointment reminders. This tailored approach helps reduce missed appointments and ensures patients receive timely care. Furthermore, reminder approaches can be dynamically adjusted by artificial intelligence in reaction to patient replies, increasing the reminders’ overall efficacy. ⁸

AI Techniques in Analyzing Patient Data for Precision Medicine

Machine Learning Algorithms, Deep Learning Approaches, Natural Language Processing (NLP) are the fundamental techniques that are used for analyzing patient data for precision medicine.

Machine Learning Algorithms

Artificial intelligence encompasses a field known as machine learning, which is a computational approach aimed at recognizing intricate patterns within datasets. These patterns can be utilized for making predictions or categorizations on previously unseen data, or for conducting sophisticated exploratory data analysis. The application of machine learning techniques to analyze the diverse data in precision medicine enables comprehensive examination of extensive datasets, ultimately leading to a deeper comprehension of human health and disease states. Arthur Samuel, while working at IBM in the 1950s, first brought the term “machine learning” into widespread use. Since its inception, machine learning has undergone significant advancements. The discipline can be further categorized into supervised and unsupervised learning, as well as reinforcement learning. ⁹

Machine learning (ML) has crucial applications in healthcare, including diagnosis through classification, patient categorization, outcome prediction, and therapy monitoring. Combining ML with genome-scale metabolic models (GEMs) offers mechanistic understanding of how genotypes relate to phenotypes. As an artificial intelligence (AI) application, ML can assist in various aspects of precision medicine, from gathering data and characterizing metabolic profiles to grouping patients and creating targeted or combination treatments. Precision medicine aims to provide individualized therapies, which not only maximizes treatment efficacy for each patient but also minimizes adverse effects and complications, resulting in substantial reductions in healthcare expenses. ¹⁰ While numerous supervised machine learning techniques have been utilized for predicting drug responses, minimal research has explored the integration of these ML methodologies into response-adaptive randomization trial designs. ¹¹

Deep Learning Approaches

The formation of deep artificial neural networks, which were modelled after the biological neural networks found in human brains, is the focus of the recently renewed field of deep learning. With some promising preliminary findings and a recent survey of medicine’s use of computer-assisted clinical and radiological decision support in the near future, deep learning has emerged as an important area of study in these fields in recent years. ¹² The application of deep learning provides a perceptive method to assist with making clinical decisions. Its ability to create its own representation—which is necessary for pattern recognition—makes it special. Deep learning is typically composed of several layers that are arranged in a sequential fashion and contain a large number of primitives, non-linear operations. This means that before the input space is iteratively transformed into a distinguishable data point, it passes through the first layer, the second layer, and the next layer (which is where the input is transformed into more abstract representations). Generally speaking, any learning paradigm may be used for deep learning. The learning technique is determined by the reason behind the use of deep learning. ¹³

DL algorithms are particularly well-suited for analyzing high-dimensional, intricate, and heterogeneous data, including omics datasets. To answer a number of problems, DL techniques have been used in genomics data in recent years. In functional genomics, DL algorithms have been used to predict the genome’s regulatory motifs and enhancer sequences using a range of data sources, including as histone modifications and chromatin accessibility. ¹⁴

Predicting drug response using genetic markers is one of the main uses of deep learning in genomics. Deep learning models can forecast an individual’s likelihood of responding to a certain medication by examining their genetic profile, enabling medical professionals to adjust treatment regimens appropriately. The influence of deep learning on precision health outcomes has been demonstrated in a number of case studies. For instance, using brain MRI data, researchers at the University of California, San Francisco employed deep learning to forecast the course of Alzheimer’s illness. Their model’s remarkable degree of accuracy in forecasting the course of the disease made it possible for earlier intervention and individualized treatment planning. ¹⁵

Natural Language Processing (NLP)

Techniques from Natural Language Processing (NLP) have become important resources for healthcare clinical text analysis. These methods give healthcare professionals the capacity to extract priceless information from the enormous volume of unstructured clinical text data, which includes patient reports, doctor’s notes, electronic health records (EHRs), and medical literature. ¹⁶

The identification of documentation representing advanced care planning measures, serious illnesses, and serious sickness symptoms is done using a range of natural language processing techniques, such as deep learning, rule-based, and conventional machine learning. ¹⁷ In the literature mining process for evidence-based therapeutic recommendations, publicly accessible tools or algorithms for important NLP technologies are examined and contrasted. ¹⁸  It is crucial to record the therapies given to a cancer patient in an organized, queryable way within electronic medical records (EMR) for the purposes of personalized medicine and treatment planning. Data in electronic medical records (EMRs) can be either unstructured (such as free-text clinical notes) or structured (such as diagnostic and procedural records). Natural language processing (NLP) techniques for extracting information from unstructured notes have been increasingly popular in recent years. These notes are a valuable source of information. ¹⁹ 

Data Sources for AI in Precision Medicine

Current clinical research and medical procedures, which are more data-intensive fields, are adopting computational intelligence approaches, particularly those that employ artificial intelligence and machine learning (AI/ML) techniques, as the practice. The integration and administration of the constantly increasing volumes of diverse data involved frequently create a number of obstacles because of the specialized nature of these disciplines. These issues are made even more pertinent by the growing significance of AI and ML, which are becoming central to the cutting edge of clinical medicine, biomedical research, public health, and healthcare policy various data sources used for AI in precision medicine has been shown in table 1. ²⁰

Table 1: Various data sources used for AI in precision medicine.

Electronic Health Records (EHRs)

The advent of genetic data in health systems and the availability of enormous volumes of digital data stored in electronic health records (EHRs) are creating new research opportunities and pathways for bettering health management. Electronic Health Records (EHRs) are patient-centered, real-time digital records of clinical care and health information that are created and kept up to date by healthcare professionals. They are intended to assist provide more thorough and precise clinical treatment by methodically gathering patient data and disseminating it among healthcare facilities and practitioners. ²¹

Both organized and unstructured EHR data formats are available, and using both kinds of data can be crucial to producing precise biotypes. The structured forms of laboratory findings, codes for billing (both for diagnosis and procedures), and a growing quantity of prescription data are easily stored in relational database systems for easy and fast retrieval. Using EHRs for phenotyping typically requires interdisciplinary cooperation. Usually, clinical informatics professionals and domain experts collaborate to develop and implement an algorithm that searches the EHR for participants with the desired phenotype and chooses instances at random for evaluation. ²² Predicting future outcomes using population and individual longitudinal data is the usual goal of EHR data analytics. Similar steps are taken in analytics, including data cleaning, identifying clinical traits, building prediction models, and clinical validation. ²³

EHRs are primarily used to facilitate the delivery of healthcare and for hospital billing; however, the data they include is usually not in a format that is easily accessible for study. It is generally known that there are numerous problems with the use of EHR for research at both the operational and data levels, even when the data is easily accessible. EHR data, for example, frequently lacks quality control and may have significant inaccuracies or high rates of missingness and redundancy. It’s also critical to remember that using retrospective cohorts has drawbacks, including selection and confounding biases. ²⁴

Genomic and Multi-Omics Data

Growing knowledge of the medical, molecular, and genetic factors influencing illnesses is likely to lead to more effective and customized medical treatments for numerous conditions. Finding predisposing, diagnostic, prognostic, and predictive biomarkers and mechanisms will eventually lead to the delivery of tailored, optimum therapy for a range of specific acute and chronic illnesses. This will be made possible by an understanding of patients’ metabolomics and genetic composition in conjunction with clinical data. ²⁵

Recent advances in omics technology have enabled unprecedented attempts to define the molecular changes that underlie the beginning and progression of a wide spectrum of complex human illnesses, including cancer. The use of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics fields has led to the proposal and celebration of multi-omics analyses as the key to the advancement of precision medicine in clinical settings. Numerous important mechanisms in cancer development, treatment resistance, and recurrence risk have been identified in precision oncology through genomics approaches and, more recently, other omics analyses. Several of these discoveries have been applied in clinical oncology to help inform treatment choices. However, the lack of widespread use of properly integrated multi-omics analysis has hindered the advancement of precision medicine. To support precision medicine-based decision-making, further work is required to build the analytical infrastructure required to efficiently create, analyze, and annotate multi-omics data. ²⁶

In this era, there is a lot of interest in using genomic data for precision medicine, particularly in the area of illness prevention. Specifically, the polygenic risk score (PRS) has been used to assess a number of illnesses, with encouraging results in some cases. It is particularly helpful for polygenic features, because the onset of disease is influenced by the minor impacts of many common variants. ²⁷

Wearable and IoT Data

In the age of precision medicine, multi-domain research must take into account a person’s life in addition to “omics” data. The five areas of life that are believed to have an impact on health are genetic, behavioural, social, environmental, and clinical. ²⁸ The Internet of Things and wearable technology are two important technological advancements that have come together to form wearable Internet of Things (IoT) devices. Essentially, they are wearable, sophisticated technological gadgets. They can collect and exchange info with various other devices and systems over the Internet thanks to their sensors and software. Smartwatches, fitness trackers, augmented reality glasses, and medical monitoring equipment are a few examples of wearable Internet of Things technologies. ²⁹

When the Internet of Things (IoT) is used effectively, it can help replace the “one-size-fits-all” approach with a “patient-like-me” approach that allows the treatment pathway to be customized based on each patient’s unique genetic profile, lifestyle, and environmental factors. More lives are saved, side effects are decreased, the cost of overt therapy is decreased, and the quality of healthcare services is enhanced by this data-driven, real-time clinical decision-making paradigm. ³⁰ The idea behind the Internet of Things is that everything will be connected to the Internet and be uniquely recognizable and reachable online. The next stage is to integrate artificial intelligence into Internet of Things systems, even if these things may directly or indirectly gather, process, or exchange data via data communications networks. In order to give real-world solutions, the Internet of Things (IoT) architecture makes use of technologies such as sensors, network communication, artificial intelligence, cloud computing, and big data. Since “wearable technology” is a catch-all term for electronics, wearables are gadgets or electronic technology integrated into clothing that is comfortable to wear. ³¹

A lot of people utilize wearable technology to test for diseases, especially arrhythmias. According to the Apple Heart Study, the Apple Watch can use PPG technology to identify asymptomatic atrial fibrillation (Afib). By monitoring vital signs and activity levels, these devices can also assist in risk assessment for individuals with known cardiovascular illnesses. These gadgets, including the Galaxy Watch Active 2, Simband, and AliveCor’s KardiaBand, have a big influence on patients’ decision-making and assist them in making wise choices. These technologies can aid in change management in addition to illness prevention and screening. ³²

Clinical Trial Data

A clinical trial’s outcome could influence the treatment of numerous future patients, change our knowledge of human biology, and have long-term financial effects on the medical field and business. Given its extensive effects, the design’s nature is crucial. The need to identify incremental therapeutic benefits between comparable treatment regimens with comparable response kinetics is addressed by the current paradigm. However, there is a noticeable rise in medications with a variety of mechanisms that operate on a growing number of pharmacologic targets due to advancements in human genome sequencing, molecular testing, and a better knowledge of cancer biology. ³³ One of the most important factors in clinical research incorporating biomarkers is the turnaround time for test results for patients undergoing genetic profiling, particularly with carcinoma clinical next-generation sequencing. When treatment decisions must be made quickly in a metastatic scenario, this is particularly crucial. ³⁴

AI-Driven Biomarker Discovery for Patient-Specific Therapies

In a number of ways, artificial intelligence (AI) contributes significantly to the advancement of our knowledge and application of genetic biomarkers. The amount and complexity of genomic data is enormous.  Pattern Recognition: In genomic data, artificial intelligence (AI) may identify subtle patterns and connections that people would miss. It may result in the identification of new biomarkers or genetic variants linked to disease. Single nucleotide polymorphisms (SNPs), insertions, deletions, and structural differences are among the genetic variants that AI systems can help identify with high accuracy. Functional Annotation: Researchers can better understand how particular genetic alterations may affect health or disease by using AI to forecast the functional effects of genetic variants. By examining several genetic biomarkers at once, AI is able to compute polygenic risk scores (PRS). These scores reveal information about a person’s genetic susceptibility to particular illnesses. ³⁵

Applications of AI in Drug Discovery and Design

The biopharmaceutical industry’s approach to creating new treatments is being revolutionized by artificial intelligence (AI), which has become a disruptive force in drug research. Target identification, hit discovery, lead optimization, preclinical research, and clinical trials are the several steps that make up the current drug discovery paradigm. Every phase offers different chances and difficulties for integrating AI. ³⁵

AI in Drug Target Identification and Validation

Drug target identification and validation has traditionally been a time-consuming and rather random process that mostly relies on industry-standard laboratory models and analysis techniques. Without necessarily knowing the precise target or mechanism of action, the majority of drug discoveries to date have adopted a phenotype-first approach, concentrating on assessing the therapeutic potential of compounds in phenotypic assays ³⁶. Drug analysis and medical diagnosis are two areas where artificial intelligence (AI) has a lot of potential. AI technology can speed up data processing and prediction, particularly in drug target identification and antiviral peptide classification, assisting researchers in finding possible therapeutic compounds more rapidly and streamlining the drug development process ³⁷. 

Deep learning methods are particularly well-suited to spotting complicated patterns in chemical structures and forecasting drug-target interactions because of their capacity to evaluate complex biological data. These models can accurately predict the biological activity of substances because they can learn hierarchical representations from enormous datasets. The use of AI in this field lessens the need for time-consuming and resource-intensive traditional in vitro and in vivo investigations. Rather, artificial intelligence (AI) models can conduct virtual screens, quickly evaluating a large number of possible compounds and identifying the ones that have the best chance of succeeding in subsequent phases of medication development. ³⁸

Virtual Screening and Drug Repurposing

Drug repurposing has become a viable approach in pharmaceutical research to find new therapeutic uses for already-approved medications, which could speed up the drug development process and lower related expenses. An important development in this area is the use of artificial intelligence (AI) into virtual screening techniques, which provide fresh ways to improve the safety and effectiveness profiles of repurposed medications. ³⁹ The learning-prediction model is created using AI in the field design, and the outcome is accurately displayed after a brief virtual screening. Using a drug-repositioning technique, AI can quickly find drugs that can fight emerging diseases like COVID-19. As an evidence-based medical tool, this technology has the potential to enhance the COVID-19 patient’s reported results, drug discovery, planning, and therapy. ⁴⁰

De Novo Drug Design

With a variety of generative models now in use, artificial intelligence (AI)-driven techniques can significantly enhance the historically expensive drug design process. In particular, generative models for de novo drug design emphasize the production of new biological substances from the ground up, which is a promising avenue for the future. It is challenging for fresh researchers to enter the field due to its rapid progress and the inherent intricacy of the drug creation process. ⁴¹ De novo drug design is a process that generates new chemical entities only from information about a biological mark (receptor) or its known active binders (ligands that have been shown to have good binding or inhibitory action against the receptor). ML techniques have been used to forecast drug discovery outcomes. Multilayer neural network computing is made possible by deep learning (DL), a branch of machine learning (ML). DL techniques including recurrent neural networks (RNN), convolutional neural networks (CNN), generative adversarial networks (GAN), and autoencoders (AE) were made possible by the huge amounts of data that were available as well as the steadily rising capability of computers. ⁴²

AI for Personalized Drug Dosage and Therapeutic Optimization

Over the past few years, personalized medicine has gained popularity as a way to enhance treatment approaches by considering a patient’s genetic composition. Reducing drug toxicity and improving the chances of drug efficacy are made possible by combining genomic data into potent new AI platforms in drug therapy. ⁴³ The difficulties of drug formulation and distribution have long plagued the pharmaceutical industry. In order to optimize formulations and delivery mechanisms, traditional methods can include expensive and time-consuming trial-and-error procedures. Predictive models produced by artificial intelligence (AI) are used to optimize medication formulations, guaranteeing that active ingredients are delivered as effectively as possible to the intended location in the body. ⁴⁴

AI-Enabled Pharmacogenomics for Tailored Drug Development

Artificial Intelligence (AI) has enormous potential to transform healthcare delivery and enhance patient outcomes when included into pharmacy practice. AI can help pharmacists with inventory management, prescription verification automation, adverse drug event and interaction prediction, and medication selection optimization. ⁴⁵ Researchers can find new treatment options with higher efficacy and reduced toxicity by simulating and anticipating the interactions between drug molecules and biological targets using AI-driven computational drug design tools. Additionally, AI algorithms analyze vast datasets, including proteomic, genomic, and clinical trial data, to predict drug-drug interactions, identify new therapeutic targets, and optimize clinical trial design—all of which speed up the creation of new medications and reduce drug development expenses. ⁴⁶

Challenges and Ethical Considerations in AI for Precision Medicine

In spite of the quick developments and the clear possibility of precision medicine’s immediate and long-term application in routine healthcare, significant ethical and social concerns should be thoroughly examined and resolved in order to guarantee that the advantages of this strategy are shared fairly and that patient rights are upheld. ⁴⁷ Numerous genetic and personal health data are being collected and used, which raises questions about who can access this information and puts patient privacy at danger. Informed consent concerns get more complex as healthcare systems depend more and more on genetic data, especially when patients might not fully comprehend the long-term effects of disclosing their genetic information. Furthermore, since people with particular genetic profiles may be treated differently, there is growing worry that precision medicine could result in genetic discrimination in insurance, healthcare, and employment. However, even while precision medicine promises innovative possibilities for improving healthcare, it also poses serious moral conundrums that need to be resolved to guarantee it’s just and moral application. ⁴⁸

Future Directions of AI in Precision Medicine and Patient-Specific Drug Design

Artificial Intelligence is a dynamic and rapidly emerging arena that has the capability to completely transform various sides of human existence. AI is becoming more and more important in the creation of new drugs. AI improves decision-making in a variety of fields, including clinical practice, pharmacology, pathology, medicinal chemistry, and molecular and cell biology. AI also has a role in the stratification and selection of patient populations. AI is clearly needed in healthcare since it helps to improve data accuracy and guarantee the high-quality care required for successful patient treatment. AI plays a key role in raising clinical practice success rates. Numerous scientific publications highlight the growing importance of AI in medication research, discovery, and clinical trials. These worries include the quality of the data, the dearth of huge datasets with thorough annotations, privacy and security problems, biases in AI algorithms, ethical and legal dilemmas, and implementation and cost barriers. However, incorporating AI into clinical medicine will enhance treatment results and diagnostic precision, help deliver healthcare more effectively, lower costs, and improve patient experiences, all of which will contribute to more sustainable healthcare. ⁴⁹

Conclusion

In conclusion, artificial intelligence has emerged as a transformative force in personalized healthcare and precision medicine. By leveraging advanced techniques like machine learning, deep learning, and natural language processing, AI enables the investigation of massive amounts of patient information from diverse sources including electronic health records, genomic profiles, wearable devices, and clinical trials. This allows for more accurate disease prediction, personalized treatment planning, drug discovery and development tailored to individual patient characteristics. AI-driven approaches are revolutionizing biomarker discovery, drug design, dosage optimization, and pharmacogenomics. While challenges remain around data privacy, ethics and equitable implementation, the integration of AI in precision medicine holds immense potential to improve health outcomes, reduce costs, and usher in a new era of personalized, data-driven healthcare. Continued research and responsible development of AI applications will be crucial to fully realizing the promise of precision medicine for enhancing patient care. 

Acknowledgement

I would like to acknowledge Amity Institute of biotechnology, Amity University Uttar Pradesh, Lucknow campus for providing us facilities to conducting this study. This research project is not funded by any specific grant from funding agencies in the public, commercial, or non-profit sectors.

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest.
Data Availability Statement

This statement does not apply to this article.
Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.
Informed Consent Statement

This study did not involve human participants, and therefore, informed consent was not required.
Clinical Trial Registration

This research does not involve any clinical trials

Authors’ Contribution

S.R. (Sweksha Ranjan): Writing – Original Draft.

A.S. (Arpita Singh): Data Collection, Analysis, Writing – Review & Editing.

R.Y. (Ruchi Yadav): Conceptualization, Supervision, Review & Editing.

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