These studies usually compare healthy and sick groups to find out more about how diseases work and who is at risk. However, this approach miss individual differences within certain groups. DNA and RNA sequencing are important tools in genetic research that help understand the causes of diseases. By looking at genetic changes, researchers can find links between specific gene variations and diseases. Using multiple genetic data points can further improve understanding^12-14.

IMPORTANCE:

Before exploring specific examples, we’ll out the characteristics of a good ML model for personalized medicine. Firstly, ML prediction must demonstrate strong performance, as measured by their accuracy in forecasting outcomes. When building a model, there’s a tradeoff between training error and test error. While collecting more training data can create a complex model with low training error, it may generalize poorly to new data, resulting in overfitting. Human investigation is necessary to understand the underlying mechanisms driving these correlations. In medicine, unexplained results are unlikely to be adopted clinically due to liability concerns. The third essential characteristic is clinical validation of ML results. Comprehensive clinical trials are required before an ML-derived model can be clinically accepted. This validation process involves three steps: cross-validation in the initial cohort, external validation in a separate cohort to address potential biases, and follow up clinical trials to conform the model’s robustness. This process is resource-intensive and time-consuming, which is why few ML-derived model have undergone through clinical validation. To recap prediction performance, interpretability, and clinical validation are three crucial aspects of machine learning in personalized medicine¹⁵.

Regenerative AI for personalized treatment in breast cancer

Precision medicine in breast cancer is leading the way in changing healthcare works, especially in diagnosing, treating, and managing the disease. This new method designed to fit each patient’s unique situation, including their specific type of tumor. It represents a big change from the old way of treating cancer with a single approach for everyone, moving toward a more customized, focused, and effective treatment plan¹⁶. Advanced AI systems are transforming breast cancer diagnosis and treatment by leveraging sophisticated computational method to enhance patient care. The process begins with the acquisition and refinement of high-resolution diagnostic images, such as mammogram and MRIs, which AI algorithms then scrutinize to extract pertinent features and detect cancerous patterns. Deep learning models, a subset of machine learning, excel in analyzing this images with remarkable precision. For example, Google’s LYNA (Lymph Node Assistant) utilizes deep learning to examine pathology slides, demonstrating exceptional accuracy in identifying metastatic breast cancer and even pinpointing cancerous region that might elude pathologists. Similarly, Mia, a cutting-edge tool developed by kheiron medical technologies, assists radiologists by providing a second opinion on mammogram readings, thereby improving early detections rates and minimizing false positives¹⁷. Navican’s AI powered precision oncology platforms seamlessly merge clinical and genomic data to craft customized treatment plans, driving superior patient outcomes and a notable 30 % boost in treatment efficacy relative to conventional methods¹⁸.

Following are the applications of AI for personalized treatment in breast cancer table 1.¹⁷

Table 1 applications of AI for personalized treatment in breast cancer

Category 1	Early detection and screening	1) AI enhanced imaging techniques (e.g., mammogram, MRIs) 2) Detection of early lesions 3) Automated image analysis
Category 2	Diagnosis and risk assessment	1) AI based pathology analysis 2) Genomic and molecular profiling 3) Predictive risk models for patient stratification
Category 3	Personalized treatment planning	1) AI driven therapy recommendations 2) Predictive models for treatment responses 3) Personalized medicine strategies
	Remote monitoring and follow-up care	1) AI powered wearable devices 2) Telehealth platforms 3) Continuous monitoring of patient health
Category 5	Drug discovery and development	1) Generative AI for novel drug candidates 2)drug repurposing and optimization
Category 6	Patient engagement and support	1) AI-driven patient education tools 2) Virtual assistants for patient queries

The emergence of Artificial Intelligence in healthcare:

Artificial intelligence (AI) has changed the healthcare field by using different technologies like machine learning, deep learning, natural learning processing, and computer vision to help doctors make better decision. AI can took at large and complicated sets of data and accurately predict health problems, which has greatly improve patient care. By quickly looking at a lot of patient information, AI helps doctors find possible health issue, create better treatment plan, and improve patient results. Some important advantages include the ability to predict health problems, after personalized treatments, and provide better care. Insights from various types of data, such as electronic health records, medical images, genetic information, and data from wearable devices, help doctors make smarter choices. As AI continues to develop, its use in healthcare will lead to new opportunities for treating patients and doing medical research, resulting in better outcome and more effective treatments. The effect of AI on healthcare is big. And its ability to improve patient care is huge, making it a vital tool for both doctors and researchers. AI in personalized healthcare helps doctors find high-risk patients so they can make targeted interventions and prevent disease. Predictive model looks at a patient data to spot early signs of chronic illness like diabetes, heart disease, and cancer. For example, these models can check blood pressure, weight, and family history to find people at risk of diabetes. This lets healthcare providers make custom preventive plans that include medicines and lifestyle changes to stop or delay diabetes from developing. It also help to find people at risk for heart disease, allowing doctors to create personalized plans that reduce the chance of heart attack or strokes, it can also detect cancer early, helping doctors make tailored treatment plans and improve patient results. By using predictive analytics, healthcare professionals can offer more patient- centered care, reduce the impact of chronic diseases, and improve overall health outcomes¹⁹

CONVENTIONAL METHOD:

The methodology for developing a framework for using AI in personalized medicine (PM) to optimize treatment plans follows a systematic approach based on the PRISMA method. The objective is to leverage AI’s capabilities in data analysis and pattern recognition to tailor treatment plans to individual patients, ensuring more effective and efficient outcomes. The results of the AI integration are evaluated. This involves testing the framework in real-world clinical settings, measuring its effectiveness in optimizing treatment plans, and assessing its impact on patient outcomes. The evaluation process includes performance metrics such as accuracy, patient satisfaction, and cost-effectiveness. Feedback is collected from healthcare providers and patient to continuously refine the AI-based framewor²⁰.

Identify problem statement

Literature Review

Define AI Integration strategy

Framework Developmen

Results Evaluation

Figure 2: PRISMA Flow chart of the study

Table 2: Research Approach Summary

Research Type	Description
Qualitative Analysis	Examines expert opinions, case studies, and policy frameworks on AI in personalized medicine.
Quantitative Analysis	Analyzes statistical data from AI-driven clinical trials, diagnostic tools, and treatment outcome.

METHODOLOGY:

1. Reasearch Approach:

The study employs a employs mixed-method approach, integrating qualitative and quantitative research methodologies. This approach allows for a comprehensive analysis of AI’s role in personalized medicine, evaluating both technological advancements and their implications on patient outcomes.

2. Data Collection Methods:

Data for this study is collected from multiple sources, ensuring a holistic understanding of AI’s role in personalized medicine.

1) Primary Data Sources: Interviews with healthcare professionals and AI researchers. Surveys were conducted among physicians using AI-driven diagnostics. Patient feedback on AI-based personalized treatment plans.

2) Secondary Data Sources: Peer-reviewed journals, conference papers, and white papers. Governmental and institutional reports on AI in healthcare. Databases such as PubMed, IEEE explore, and Google Scholar.

Table 3: Data Collection Sources

Data Source	Description
Primary Data	Surveys, Interviews, Case Studies
Secondary Data	Published Research, Reports, Databases

3) AI Algorithms & Model Evaluation Criteria:

To assess the efficiency of AI in personalized medicine, various machine learning (ML) and deep learning (DL) models are evaluated. These models are assessed based on accuracy, interpretability and clinical usability.

Table 4: AI Models Evaluation Criteria

Evaluation Metric	Description
Accuracy	Measures prediction correctness of AI models.
Interpretability	Assesses how understandable the model is for clinicians.
Scalability	Evaluates the ability of AI models to handle increasing data volumes.

3. Ethical Considerations and Bias Analysis:

AI in medicine raises ethical concerns, particularly regarding bias, privacy, and accountability. This study includes an analysis of ethical challenges faced by AI-driven personalized medicine.

Table 5: Ethical Considerations in AI medicine

Ethical Concern	Explanation
Bias in AI Models	AI algorithms trained on biased data can lead to unequal healthcare outcome.
Data Privacy	Ensuring patient data security in AI-driven systems.
Regulatory Compliance	AI in healthcare must comply with HIPAA, GDPR, and other legal frameworks.

4. Validation of AI in personalized medicine:

The study evaluates the effectiveness of AI-driven personalized medicine through real-world case studies and success metrics.

Table 6: AI Validation Approaches

Validation Approach	Details
Clinical Trials	Examining AI-powered diagnostic and treatment recommendations in real-world settings.
Patient Outcomes Analysis	Assessing improvement in treatment effectiveness through AI interventions.

OPPORTUNITY:

Prospects despite the challenges, AI presents immense opportunities for advancing personalized medicine. The continuous development of AI-driven methodologies, combined with collaborative efforts between healthcare providers, researchers, and technology companies, will drive the next wave of innovations in precision medicine⁷.

Unlocking AIs potential in pathology:

Deep learning architectures, such as deep convolutional neural networks, have achieved remarkable success in image classification and analysis. Digital pathology combines advanced slide-scanning techniques with AI-driven approaches to detect, segment, score, and diagnose digitized whole-slide images with unprecedented accuracy. By harnessing the power of neural networks algorithms and large datasets, researchers have trained models to classify skin lesions with quality standards comparable to current pathology practices. This intuitive image-based analysis has vast potential for enhancing early detection and cancer prevention²¹.

Future applications include:

1. Streamlined pathology workflows:

AI-powered modules will replace traditional steps, improving efficiency and accuracy²¹.

2. Accurate drug response prediction:

Digital pathology labs will enable more precise prognosis and treatment planning.

The integration of AI and digital pathology is transforming the field, enabling more accurate diagnoses, and enhanced cancer care

In Ophthalmic disease:

AI combined with teleophthalmology can help make eye care more fair and equal for everyone. AI driven screening strategies are poised to revolutionize population screening for blindness- causing eye disease in china, offering a cost effective solution. Routine screening could provide robust economic evidence for informed policy-making and large-scale promotion. While ophthalmic AI and telemedicine hold promise, widespread adoption faces significant barriers. Clinicians must balance innovation with evidencebased protocols to ensure improved patient outcomes. By emerging AI-driven solutions, healthcare professionals can enhance screening efficiency, accuracy, and accessibility, ultimately reducing the burden of blindness and improving vision care in china²⁴.

Explainable AI:

Its focuses on making AI decisions clearer and easier to understand. This helps doctors and patients trust the technology more, which makes it easier to use in healthcare settings¹⁹

Federated learning:

Federated learning enables AI models to tap into diverse datasets across multiple healthcare institutions while prioritizing patient data security and confidentiality. By facilitating collaborative research without data sharing. Federated learning unlocks new insights, accelerates medical breakthroughs, and improves model accuracy while maintaining data protection¹⁹AI driven virtual assistants

AI-powered chatbots and virtual assistants revolutionize healthcare accessibility by delivering personalized health recommendations, streamlining medication management, and fostering patient engagement, ultimately enhancing the overall quality and reach of healthcare services^22,23.

Blockchain for data security:

The fusion of blockchain and AI creates an unbreachable synergy, securing medical records with unparalleled transparency and immutability, safeguarding sensitive patient data and ensuring trust in healthcare’s digital backbone^19,22,23.

Precision oncology:

AI is transforming cancer care by decoding biomarkers, forecasting treatment efficacy, and tracking tumor dynamics in real-time.

Empowering oncologists to tailor therapies and boost patient outcomes, ultimately rewriting the cancer treatment playbook²²

ADVANTAGES:

Ai revolutionizes personalized treatment by decoding individual patient data, curating tailored therapieand dynamically adjusting treatment pathways in real-time, ensuring precision, efficacy, and safety, while optimizing resource allocation and responding swiftly to treatment nuances.

Drug recommendations:

Pharmacogenomics and AI converge to craft tailored treatments by analyzing genetic variations in medication responses. Future therapies will leverage AI algorithms to integrate patient’s genetic, transcriptomic, proteomic, epigenetic, and lifestyle data for precise medication. Advanced AI techniques like deep learning and graph embedding enhance drug targeting, saving time and improving patient outcomes. However, AI-driven recommendations face explainability challenges, hindering model, interpretability is crucial for boosting AI credibility and adoption in healthcare, ultimately unblocking its full potential.

Remote monitoring:

AI-powered remote patient monitoring revolutionizes healthcare by leveraging wearable devices and sensors to track patient’s vital signs in real-time. This technology enables personalized treatment plans, reduces medical burdens, and enhances patient autonomy. During the COVID-19 pandemic, AI-driven imaging technologies alleviated healthcare worker exposure and streamlined patient screening. Intelligent assistants, like chatbots and robots, support patient with chronic conditions, such as diabetes management, by tracking physical Data and providing timely interventions. While AI- driven remote monitoring improves healthcare outcomes, prioritizing health data privacy and security through robust safeguards is crucial for building trust and ensuring safe, effective care¹⁹

Improve treatment outcomes:

Personalized treatment plans that AI are more likely to work than traditional methods. This approach ensures that each patient gets care that fits their unique situation. Experts believe that this method leads to better health results because it helps find the right medicine and dose without relying on guesswork. Using AI to look at many types of data helps create treatments that are more tailored to what each patient needs, making the overall treatment more effective^25,27

Early disease detection

AI revolutionizes early disease detection by analyzing complex genetic and medical data, identifying hidden biomarkers for disease like cancer, diabetes, and heart disease. Intelligent models pinpoint subtle signs. Enabling physicians to intervene early when treatments are most effective, significantly improving patient prognosis and health outcomes^26,27

DISADVANTAGE AND CHALLENGES:

Although AI has proven to be effective in both diagnosis and treating medical conditions, there are some disadvantage associated profession instead of improving it. This concern comes from the belief that AI based medical practices may struggle with complex tasks. Also, critics argue that AI can’t show medical empathy, which is very important to those who are against using it. The idea that medicine needs wisdom rather than just intelligence supports this opposition. For example, the traditional doctor-patient relationship shows that showing empathy can make patient feels cared for and understood. However, computers can’t express empathy. So it’s hard to provide complete caring medical care. In addition to not being empathetic, AI tools are expensive and require special training to use. They are also limited in some areas because they don’t have enough good data to keep learning, it’s likely that hospitals wanting to use AI would need lot of money and ongoing training for their staff. Therefore, only large hospitals with big budgets would able to use AI- based medical tools. Leading to unfair differences in healthcare access. Because of these reason, some doctors and researchers prefer human-centered, rather than AI-assisted, medical care. Overall, the biggest fear about using AI in medicine is that it might make medical care less personal and eventually replace doctors with computers²⁸.

Data privacy and security:

Collecting and handling a large amount of patient data, such as genetic information, makes data privacy and security very important for AI systems to work properly. Real-world organizations that handle patient information must follow specific laws, like HIPAA and GDPR, to reduce the chances of data leaks, breaches, or misuse, this is because patients need to be sure their information kept safe, with encryption to stop others from accessing it, and the systems must also follow all legal requirements ²⁷The integration of AI in healthcare raises significant concerns about patient data privacy and confidentiality, involving sensitive health information. Question arises about authorized access, privacy policies, and the risk of data breaches or misuse. Implementation robust data encryption and access controls is crucial to mitigate these risks. Strengthening governance practices can foster trust in AI- driven healthcare services. Key challenges include ensuring data security, maintaining patient confidentiality, and preventing unauthorized access or exploitation of sensitive information²⁹.

False prediction:

The integration of artificial intelligence in healthcare is fraught with challenges that necessitate meticulous consideration. A significant hurdle is the scarcity of comprehensive, well-annotated datasets, particularly for complex conditions like cancer, which hampers the efficacy of machine learning models. The alarming rate of false positives in melanoma detection underscores the imperative for rigorous validation. As AI applications proliferate in health technology assessments, they outstrip available data, raising pressing concerns about potential consequences. Data privacy and security emerge as critical issues, demanding vigilant attention amidst the delicate balance between innovative and risk. The reliance an algorithmic decision-making renders smart systems susceptible to security breaches with potentially severe repercussions. Moreover, the risk of generating erroneous outcomes highlights the need for robust oversight mechanisms. Overlooking relational dynamics or ignoring side effects stemming from flawed algorithms can exacerbate existing vulnerabilities. Furthermore, the absence of human empathy in AIdriven healthcare solutions poses significant challenges, particularly when cultural nuances in mental health are not considered, underscoring the need for a thoughtful approach to AI integration in healthcare²⁹

Computational cost:

Advanced models like transformers require significant computational resources. Cloud based solutions and optimized algorithms can mitigate these cost³⁰.

Problem in AI- based decision support:

Many AI-base decision support tools use deep learning and neural network algorithms. These algorithms can give very reliable predictions if they are trained on a large amount of data. However, it can be difficult to understand how the input data leads to the predictions. This is known as the ‘Black Box’ problem, which can make people unsure or hesitate to trust the predictions, especially when real people’s lives are involved, also not all AI methods are meant to find cause-and-effect relationships between inputs and outputs. Instead, they often just find patterns or correlations. This works well for making accurate predictions, but it’s not enough if the goal is to find something like a drug target that, when changed, cause and effect is essential³¹.

FUTURE PROSPECTS:

The fusion of AI and precision medicine is revolutionizing personalized care, tackling complex challenges and unlocking new possibilities. By harnessing the power of AI, healthcare can become more precise, effective, and tailored to individual needs³². Future research directions in AI and pharmacogenomics should focus on three key areas: diversifying research to include underrepresented AI/AN populations and characterizing relevant genetic variants, advancing AI algorithms for precise drug response prediction and biomarker discovery, and fostering interdisciplinary collaboration to drive innovation in personalized medicine and healthcare managements⁵. The integration of AI in personalized medicine revolutionizes healthcare by leveraging cutting-edge data analytics, machine learning, and clinical expertise. A comprehensive framework combines data acquisition, preprocessing, model development, and clinical decision support, ensuring individualized treatment plans based on unique patient characteristics. Key aspects include transparency, explainability, and ethical considerations like data privacy and algorithms fairness. This framework enables clinicians to make informed decision, improves therapeutics outcomes, minimize adverse effects, and enhances patient satisfaction. Future research directions include enhancing AI model generalizability, integrating social determinants of health, and developing privacy-preserving techniques. Broad clinical trials and stakeholder collaboration will be crucial for successful implementation. Ultimately, AI-powered precision medicine holds immense potential for transforming healthcare, improving patient outcomes, and advancing clinical decision-making³³. The future of personalized medicine looks promising with AI integration. Advanced machine learning and deep learning capabilities will enable AI to accurately diagnose diseases, identify them at an early stage, and recommend customized treatment plans that cater to each patient’s unique needs³⁴. Additionally, AI will keep playing a big role in making personalized medicine available around the world. It will help places with few healthcare workers get remote health checks and diagnostic tools. As AI technology keeps improving, these tools will unlock new possibilities in healthcare. They will help provide the best possible, affordable, and efficient medical care to more people. This will make healthcare easier to access and more tailored to individual needs²⁷. Several wearable tools and sensors have been created to monitor various body-related measurements in real time. Personalized medicine can be greatly improved by using wearable technologies that are part of smart drug delivery systems. These devices gather and send real-time data about biomarkers and body functions to doctors continuously. They can give real-time alerts, and allow for monitoring without direct contact with the patient. The sensors can convert data related to biochemical, electrical, or mechanical aspects of the body. Because they are lightweight, they offer a high level of comfort and do not affect the accuracy of the data. A wide range of body-related parameters can be monitored directly through wearable technology, such as heart rate, _pH levels, body temperature, blood pressure, movement, and more. Many diseases can be detected early with the help of these devices. These can be used for variety of purpose, including self-health tracking for fitness lovers, managing chronic conditions, detecting COVID19, or monitoring heart health and fainting episodes. Since wearable technology is either affordable or widely available. It creates many opportunities for new ideas and applications. Some clothing can also include skin sensors as part of its design²⁹.

CONCLUSION:

Precision medicine is moving forward, but there are still many challenges ahead. These challenges need more helpful analytic tools, technologies, database, and methods to improve how clinical, laboratory, and public health systems connect and work together. They also need to address ethical and social concerns about protecting the privacy and security of healthcare and omics data, while maintain a good balance. This will require better ways to handle large volumes of data that are created, as well as earlier agreement and useful information from data that has already been analyzed. Most of what is done today is done by hand and takes a lot of time, whether it’s getting healthcare data from clinical systems, finding common and rare genes changes, figuring out how metabolites behave using listed features and issues, looking at how genetic changes relates to metabolites levels, studying biochemical pathways in metabolites with patterns from multiple data types to find candidate genes, and managing and combining healthcare, epidemiologists, and omics data of every step of data entry, creation, and analysis. Developing new AI and ML-based big data platforms has the potential to change medicines in a big ways and improve the quality and delivery of healthcare by smartly analyzing large amounts of structured clinical data. However, this is also bringing new challenges in storing, processing, sharing, and organizing data, as well as helping us understand biology better.

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Received on 01.11.2025 Revised on 02.12.2025

Accepted on 26.12.2025 Published on 10.04.2026

Available online from April 13, 2026

Asian J. Res. Pharm. Sci. 2026; 16(2):191-198.

DOI: 10.52711/2231-5659.2026.00030

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

INTRODUCTION: