Personalized Medicine through AI-Driven Diagnostics
**DOI :****10.17577/IJERTV15IS031006**
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Vageesha Vats, Gunn P Jain, 2026, Personalized Medicine through AI-Driven Diagnostics, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026
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* **Authors :** Vageesha Vats, Gunn P Jain
* **Paper ID :** IJERTV15IS031006
* **Volume & Issue : ** Volume 15, Issue 03 , March – 2026
* **Published (First Online):** 27-03-2026
* **ISSN (Online) :** 2278-0181
* **Publisher Name :** IJERT
* **License:** This work is licensed under a Creative Commons Attribution 4.0 International License
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#### Personalized Medicine through AI-Driven Diagnostics
Vageesha Vats
23BCAR01961, BCA-Cybersecurity Deaprtment Of Cs/It
Jain Deemed To Be University Bangalore.
Gunn P Jain
23BCAR02042, BCA-Cybersecurity Deaprtment Of Cs/It
Jain Deemed To Be University Bangalore.
Abstract – Artificial intelligence is transforming healthcare. Shifting from one size fits all care to truly individualised treatment. Due to their wide variations in genetics, clinical history and environment and lifestyle, peoples reaction to same medication differ significantly. As genomic, sequencing, electronic health records and continuous wearable data becomes more common, AI systems can use this multi-model data to predict individual drug responses and guide prescribing. In particular, AI driven pharmacogenomics uses machine learning to combine genetic variations with non-genetic factors such as diet, physical activity, and other behaviours to produce personalised recommendations for medication selection and dosage instead of population average regimens. This paper explores how AI based diagnostic and decision-support systems can link diagnostics to treatment by recommending medications according to each patients genes and lifestyle profile, aiming to reduce adverse drug reactions and improve therapeutic effectiveness. It also highlights key challenges including model, transparency, data, privacy, bias, regulatory oversight, and clinician acceptance that must be addressed for safe, equitable and real-world adoption of AI-driven personalised prescribing.
1. INTRODUCTION
Individualized care is increasingly replacing generic treatments in todays data-rich, technologically advanced healthcare environment. Conventional one size fits all prescribing frequently overlooks important variations in genetics, medical history, environment and daily routines which can result in inconsistent treatment results and preventable side-effects. Thanks to the convergence of wearable sensors, genomics, artificial intelligence, and electronic health records, it is now feasible to analyze these factors collectively and create individualized medical regimens. In this regard, AI driven personalized medicine focuses on employing algorithms to determine how patients with various gene profiles and lifestyles reacts to medications and then suggesting the best drug and dosage for each individual.
The goal of the study is to investigate how AI can be used to transition from general diagnostic support to personalized prescribing in which treatments are selected and modified based on patients genes and personal life choices. AI systems can support more accurate, safer, and efficient medication
choices, than standard guidelines alone by combining geonomics (the study of how genes affect drug response) with the real-world data like activity levels, diet, sleep, and comorbidities. AI driven decision support tools have the potential to revolutionize the way clinicians chose medication, particularly for chronic conditions requiring long-term closely monitor therapy, as healthcare systems look for ways to improve outcomes while reducing trial and error prescribing. However, there are significant research questions regarding data, quality, model, reliability, patient, privacy, fairness, and practical usability in clinical settings when developing and implementing such AI based prescription systems. It is necessary to comprehend how to incorporate lifestyle and genomic data into clinical workflows without overburdening physicians, how to make sure AI recommendations are clear and understandable, and how to prevent the reinforcement of pre-existing biases in healthcare data.
In order to demonstrate how AI driven personalize prescribing can improve patient care while assuring to safety, ethical, and legal requirements, this study focuses on comprehending the balance between technological capability and responsible implementation.
Research objectives
AI Powered Risk Assessment and Diagnostics
Analyze how AI models classify patients into risk groups that inform treatment planning and use clinical, imaging, and laboratory data to identify disease early.
Using Genomics to Customize Prescriptions
Examine how AI can combine clinical variables and genetic variations to forecast each persons reaction to drugs and recommend the best medication and dosages.
Optimization of Lifestyle-Aware Therapy
Examine how AI systems can be used to continuously improve and customize medication regimens by in cooperating data from variables and digital health tools ( such as activity, sleep, and appearance patterns).
Practical, Ethical, and Regulatory Aspects
Examine the main obstacles to the safe and fair implementation of AI driven personalize, prescribing an actual healthcare
systems, such as transparency, data, privacy, biases, regulation, and clinical training.
2. LIMITATIONS OF THE STUDY
While the exploration of AI-driven personalized prescribing holds promising opportunities for improvement of healthcare services, the findings of the research also come with a series of limitations that could affect the generalization of the results.
Conceptual and Non-Experimental Scope
This research paper appears to be conceptual and based on literature findings rather than actual experiments or trials. Accordingly, the findings of the research paper are based on theoretical potential and outcomes of the application of AI- driven personalized prescribing.
Data Availability and Representativeness
This Research paper assume the availability of high-quality genomic, clinical and lifestyle information. However, the quality of the information obtained from various sources can be limitation to the generalization of the findings of the research paper.
Simplification of Technical Models
In order to make the research paper understandable to a wider audience, the technical models of AI architecture and algorithms used in the research paper are presented in a simplified form.
Regulatory and Ethical Generalization
In the study regulatory, ethical and governance issues are highlighted at a general level and across several regions. It is important to know the regulatory system very significantly between different countries and are constantly changing.
Limited Coverage of Clinical Specialties
Although some specialties like cancer treatment, heart disease treatment, and managing chronic diseases are highlighted in the study, the studies not exhaustive in terms of covering all medical specialties or drug types.
Assumptions Used in the Study
There are some assumptions used in the study regarding the level of technology and AI adoption by different individuals and institutions. In reality, the level of technology and AI adoption may vary significantly between different individuals and institutions.
Incomplete Analysis of Long-term Impact
Long-term clinical, economic, and social implications of AI- based personalized prescription are beyond the scope of the study.
3. SCOPE OF THE STUDY
This current study is based on exploring and understanding the concept, design and practical applications of AI-based personalized prescribing within modern healthcare settings. The overall aim of the study is to analyze how artificial intelligence can be used to improve clinical decision-making to move beyond traditional. One size fits all medicine towards personalized medicine based on individual genetic profiles and lifestyles.
It examine how AI-based personalized prescribing can work and practice from the collection of individual information to AI-based analysis to create personalized medicine recommendations.
Moreover, the current study is based on exploring how practical AI-based personalize prescribing can be within real-world healthcare settings. This involves understanding the willingness of individual healthcare, practitioners and institutions to adopt AI-based personalize, prescribing, as well as the awareness and willingness of patience to use the individual, genetic and lifestyle information to make prescribing decisions.
In addition to that, the current study is based on evaluating the potential of AI-based personalize prescribing to create innovation within precision, medicine and genomics. This involves understanding the potential benefits and challenges associated with creating AI-based personalized prescribing models to improve window individual patient care.
Overall, the scope of the project is to gain meaningful insights into how AI-based personalize prescription systems can fit into contemporary healthcare, making treatment, more precise, safer, and more individualized, while also contributing to the broader evolution of data driven and patient centered medicine.
4. LITERATURE REVIEW
WHO- guidance on artificial intelligence in health (2021 – 2024).
FDA -AI/ML in software as medical device. Regulatory expectations for life-cycle management, validation, and reporting of AI-enabled diagnostic and decision support tools.
Reviews on AI in Personalized Medicine and Precision Health (2020-2025).
Overview of applications and diagnostics, risk prediction, and individual treatment planning, including oncology, cardiology, and neurology.
AI in Pharmacogenomics and Patient Specific Drug Response (2023-2025).
Studies that integrate gene variant with machine learning models that help predict the drug efficiency toxicity and optimal dozing for individual patients.
Multi-model and Genomics Plus Lifestyle Models for Precision Medicine (2024-2025).
Work on combining clinical data, genomics, imaging, and lifestyle, or environmental data to improve patient stratification and treatment personalization.
Generative AI and Personalized Medicine and Treatment Planning (2024-2025).
Research on using generator models to summarize clinical knowledge, simulate, patient, responses, and support Tailored therapy design.
Clinical Studies on AI-Enabled Diagnostics and Treatment Guidance (2023-2025).
Evidence of improved detection, risk scoring, and treatment, planning and oncology, endoscopy, and other specialty using AI tools integrated into clinical workflows.
Policy and Governance Analysis for AI in Health (2023-2025). WHO, OECD, and regional policy papers discussing health- system, impacts, data governance, fairness, and macro-level considerations for AI-driven personalized care.
Critical Perspective on Bias, Over- Customization, and Real- World use.
Commentaries highlighting risks of algorithmic bias, limiting generalizability, over-reliance on personalization, and the practical barriers to AI deployment and diverse healthcare settings.
5. RESEARCH METHODOLOGY
Artificial intelligence (AI) has become one of the most significant technologies changing modern healthcare systems. In recent years, researchers have explored how AI can support the transition from traditional medical practices to more personalize approaches. Conventional healthcare models often follow a generalized treatment strategy in which similar therapies are provided to large groups of patients. However, individuals differ in terms of genetics, medical history, environmental factors and lifestyle choices. Because of these
variations, personalized medicine has gained significant attention as it focuses on designing treatments that is customized according to the individual needs of each patient.
Several studies have highlighted the ability of AI technology to analyse large volume of healthcare data efficiently. Machine learning and deep learning techniques are capable of processing information obtained from electronic health records laboratory reports medical scan imaging systems and genomic databases. By examining these complex datasets, AI-models can detect patterns and correlations that may not be immediately visible to health care professionals. As a result AI can access physicians in making faster and more accurate clinical decision.
Researchers have also examined the role of AI in early disease detection. AI-based diagnostic tools have shown strong effectiveness in identifying illnesses such as cancer, cardiovascular disease and neurological disorders. These systems are especially useful for analysing diagnostic image such as MRI scan, CT scan and X-rays by detecting small abnormalities in imaging data, AI model can support doctors and identifying diseases at earlier an stage, which can significantly improve treatment outcomes and survival rates.
Another important area of research is Pharmacogenomics, which focuses on understanding how genetic differences influence individual responses to medications. AI technologies can examine genomic data together with clinical information to estimate how patients might respond to certain medications. This capability allows healthcare providers to select treatments that are most suitable for each patient while reducing the risk of adverse drug reactions. As a result, a contributes to the creation of more accurate and effective treatment strategies.
Recent research as also emphasized the importance of integrating multiple sources of healthcare data. Advanced AI systems are capable of combining clinical records, genomic data wearable device information, and lifestyle factors to generate more comprehensive diagnostic insights. This integrated analysis enables healthcare providers to gain better knowledge of patient condition and assists in creating individualized treatment plans.
Even though AI provides significant advantages in healthcare, a number of challenges have been identified in existing literature. One major concern is safeguarding patient data, as healthcare and information is highly sensitive and requires strict privacy measures. In addition, algorithm bias may occur if AI models are often trained using datasets that do not properly represent diverse population. Another challenge involves the lack of transparency in certain AI models, which
may reduce the trust amount healthcare professionals if the decision-making process cannot be clearly explained.
To address this issues, international health organisations and regulatory bodies have begun developing guidelines for the responsible implementation of AI technology in healthcare. These guidelines stress the significance of ethical AI development, transparency, fairness, and patient protection. Most researches agrees that AI should function as a decision support tool them enhances the export is of healthcare professionals rather than replacing them.
Overall, the literature suggests that AI driven diagnostic technologies have the potential to significantly improve personalized medicine by enabling early disease detection, improving diagnostic accuracy, and supporting individualized treatment strategies. However, further research, stronger regulatory frameworks, and continued technology advancements and necessary to ensure the safe and effective integration of AI into health Care systems.
6. PROPOSED METHODOLOGY
The proposed methodology for this study focuses on examining how artificial intelligence can be used to support personalized medicine through advanced diagnostic systems. The main objective of this methodology is to analyse how AI technologies can interpret large volumes of healthcare data and assist medical professionals in identifying diseases, predicting risks, and development treatment plans that are tailored to individual patients.
This research follows a structured approach that combines Data collection, data processing, AI model development, and performance evaluation. Each stage of the methodology contributes to building a system that can analyse complex healthcare information and provide meaningful insights for medical decision making.
The first stage involves Data collection. Health Care data from various sources to ensure a complete analysis of patient health conditions. These sources include electronic health records that contain patient medical histories, databases that provide genetic information, laboratory test reports, and medical scan datasets such as X-rays, CT scans, and MRI scans. In addition, lifestyle information collected from wearable health tracking devices may also be included to better understand patient behaviour and health patterns.
After the data is collected, the next stage is data preprocessing. Raw health Care data often contains incomplete entries, inconsistencies, or error that may affect the performance of AI models. Therefore, pre-processing is necessary to clean the data
sets and prepare for it analysis. The step involves removing duplicate records, correcting in accurate information, and transforming the data into an organized format suitable for analysis by machine learning models.
The following stage focuses on AI model development. At this stage, machine learning and deep learning techniques are implemented on the prepared dataset in order to detect patterns associated with diseases and treatment outcomes. These algorithms are trained using previously recorded medical data so that the systems can learn how different factors influence disease diagnosis and patient responses to treatments. Predictive models such as neural networks and classification algorithms may be used to perform this analysis.
Once the models are developed, the research proceeds to the training and testing phase. The datasets are split into two parts: a training data set and a testing dataset. The training set enables the AI system to identify patterns and correlations in the data, whereas the testing set is used to measure how effectively the model performs on new, unseen information. This step helps ensure that the system is reliable and capable of making accurate predictions.
To determine the effectiveness of the AI systems, performance evaluation metrics are applied. The evaluation measures include accuracy, precision, recall, and the F1 score. These indicators help measure how well the model identify diseases and predicts health outcomes. The results generated by the AI systems are also compared with traditional diagnostic methods to determine whether the use of AI improves clinical decision- making.
The final state involves integration with clinical decision support systems. In this stage, the AI generated insights are incorporated into tools that assist health care professionals during diagnosis and treatment planning. Rather than replacing human expertise, the AI system functions as a supportive technology that enhances the ability of doctors to interpret complex medical data and make more informed decisions.
By following this methodology, the study aims to demonstrate how AI driven diagnostic systems can contribute to improved health care outcomes. The approach highlights the potential of artificial intelligence to enable earlier disease detection, more accurate diagnoses, and personalized treatment strategies while maintaining strong ethical standards related to patient privacy, transparency, and fairness.
7. CONCLUSION
The concept of personalized prescribing to the assistance of AI technology signifies a major leap forward in the effective union
of technology and patient centric medicine. This study seeks to explore the capabilities of artificial intelligence to transcend the conventional "one-size-fits-all" approach to all medicine by developing a system that can provide personalized prescription of medicine according to the genetic makeup of the patient, their medical history, as well as their lifestyle pattern. This can be accomplished through the effective union of Pharmacogenomics, electronic health records, as well as data from wearable technology or other digital health tools. According to the discussion, the potential of the concept of personalized medicine through assistance of artificial intelligence technology can be deemed highly promising despite the various challenges that need to be addressed. According to the research, the development of effective artificial intelligence technology can only be accomplished through the effective collection of diverse data that can be used to provide fair recommendations. However, the research proved that the concept of personalized prescribing through assistance of artificial intelligence technology can be deemed highly effective as a potential direction for the future of medicine. From a social perspective, the research signifies the importance of the effective use of data to ensure that the benefits of personalized medicine can be enjoyed by different patient population. The potential of the concept to provide continuous improvements to the effectiveness of the medicine can be deemed highly promising through the assistance of real- time lifestyle patterns. In conclusion, the concept of personalized prescribing through the assistance of artificial intelligence technology signifies the future of medicine that can be deemed highly effective in the development of a precise healthcare ecosystem.
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