Abstract

Precision medicine is a method of approaching healthcare which moves away from a one-size-fits-all style. Rather than using the same treatment for everyone, precision medicine focuses on an individual’s genetics, the small signals in their cells, the environment around them and their daily habits. Precision medicine is built on modern tools and technologies like next-generation sequencing, single-cell sequencing, multi-omics, electronic health records, artificial intelligence, wearable devices, organ-on-a-chip technology and CRISPR gene editing. These allow doctors to gather detailed data about each patient and use that information to decide the best treatment. This paper discusses the current state of precision medicine, explaining how it is utilised in cancer treatment (for example, HER2- and EGFR-targeted drugs), in breaking down types of diabetes that arise from single or multiple genes, in rare brain diseases where a single patient receives a custom therapy such as milasen for Batten disease, and in pharmacogenomics. This paper also addresses challenges that precision medicine faces, including fragmented data, high implementation costs, limits of current models, and ethical questions about privacy and gene editing. Drawing together multiple studies and real patient stories, this paper shows how precision medicine is moving from a scientific concept towards everyday individualised care.

Introduction

DEFINITION AND CORE PRINCIPLES

Traditional medicine often relies on a standardised approach to diagnosing and treating patients, where individuals with the same disease may receive similar treatments (Johnson et al., 2021). However, patients can differ considerably in their genetic makeup, biological characteristics, environmental exposures and lifestyles, resulting in differences in disease risk and responses to treatment. Precision medicine, sometimes known as personalised medicine, is an innovative approach to tailoring disease prevention and treatment that takes into account differences in people’s genes, environments and lifestyles (FDA, 2019). Rather than assuming that the same treatment will be equally effective for everyone, precision medicine aims to identify the healthcare strategies that are most appropriate for specific individuals or groups of patients. The core principle of precision medicine is therefore individualisation. It recognises that patients are biologically diverse and that these differences can influence both disease development and treatment outcomes. To account for this variation, precision medicine integrates multiple sources of patient-specific information, including genomic, molecular, clinical, environmental and lifestyle data to create more personalised treatment. Ultimately, precision medicine shifts the focus of healthcare from treating the “average” patient towards understanding the unique characteristics of each patient.

WHY PRECISION MEDICINE, WHY NOW

The growing interest in precision medicine can be attributed to both the limitations of conventional healthcare and the rapid development of technologies that make personalised healthcare increasingly possible. Traditional medical approaches often rely on population-level averages, but individuals with the same diagnosis may differ substantially in their disease progression, treatment response and risk of adverse effects. This has created a need for approaches that can better account for individual biological variation. At the same time, recent advances in genomic sequencing, molecular profiling, artificial intelligence (AI) and big-data analytics have made it increasingly feasible to collect and analyse large quantities of patient-specific information. These technologies enable healthcare professionals to identify genetic, molecular and clinical characteristics that can be used to predict an individual’s disease risk, guide treatment selection and anticipate their likely response to therapy. By enabling the integration and interpretation of diverse patient-specific data, these technological advances support a shift from a one-size-fits-all approach towards more personalised prevention, diagnosis and treatment.

BIOLOGICAL DIFFERENCES BETWEEN PEOPLE

Studies have shown that individuals differ biologically at several levels, including their genetic, molecular, physiological, environmental and behavioural characteristics. Advances have revealed substantial inter-individual variation in disease processes, suggesting that patients may require different approaches to disease prevention, monitoring and treatment (Goetz & Schork, 2018). Age, genetic variation, sex and differences in the immune system may contribute to differences in disease susceptibility and manifestation between individuals (Pereira et al., 2021). Additionally, lifestyle and environmental factors can interact with an individual’s biological characteristics and influence their health and response to treatment (Wang & Wang, 2023). Consequently, patients with the same disease may experience different disease trajectories or responses to treatment. Recognising this biological variation provides an essential foundation for precision medicine (Goetz & Schork, 2018; Wang & Wang, 2023).

AIM AND SCOPE

This article aims to summarise the current state of precision medicine, drawing together its technological drivers, clinical applications and challenges shaping its adoption. Precision medicine is a rapidly evolving field where the literature is often fragmented; this article provides a synthesised review of where the field stands and what next steps are emerging across the industry.

The article covers the enabling technologies that make it possible to map individual genomes and use that data effectively: data collection through wearables and sensors, AI models, organ-on-a-chip systems and genomic sequencing platforms. It then presents applications in cancer treatment, diabetes, rare diseases and pharmacogenomics, before examining the challenges and ethical concerns the field faces.

It should be noted that this article is a synthesised narrative review of existing literature and case studies rather than a systematic review or meta-analysis. It does not aim to cover every topic exhaustively to the depth of specialised research.

Literature Review: Technologies in Precision Medicine

ELECTRONIC HEALTH RECORD

Electronic health records (EHRs) are a core technology in precision medicine because they bring together the longitudinal patient information required to personalise care. EHRs track data including diagnoses, medications, laboratory results, imaging, surgical history and patient-reported outcomes, allowing clinicians to observe how a patient changes over time. This matters in precision medicine because personalised care requires more than genomic data alone – it also requires knowledge of a patient’s prior conditions, medications and treatment responses. EHRs therefore provide the contextual foundation for translating scientific findings into direct patient benefit (Fernandez-Breis et al., 2016).

EHRs also enable research in precision medicine by allowing analysis of data from large patient populations. When systems are well-integrated, EHRs can help researchers identify patient subgroups with particular treatment responses, understand disease progression and develop predictive models. These capabilities are enhanced further when EHR data is combined with genomic data, linking biological profiles with real-world clinical outcomes (Fernandez-Breis et al., 2016; Song et al., 2024).

However, the use of EHRs in precision medicine faces several challenges: incomplete records, systems that lack interoperability between institutions, variable data quality and privacy concerns that can restrict the sharing of data required for research. Sensitive information is often buried in unstructured clinical notes rather than structured fields, complicating data extraction and analysis. Despite these limitations, EHRs remain essential to precision medicine because they provide clinical context that genetic and molecular data alone cannot offer, and they are the key bridge between scientific research and real-world patient care (Fernandez-Breis et al., 2016; Song et al., 2024).

ARTIFICIAL INTELLIGENCE

Artificial intelligence refers to the science and engineering of developing systems capable of mimicking cognitive functions such as learning, problem-solving and pattern recognition (Bajwa et al., 2021). It encompasses subfields including machine learning (ML) and deep learning (DL), which extend analytical capabilities well beyond conventional statistical approaches (Bajwa et al., 2021; Fahim et al., 2025).

The integration of AI with EHRs allows more accurate and rapid disease detection, since AI systems can recognise disease patterns across large datasets and correlate them with individual patient records (Fahim et al., 2025). In skin cancer diagnosis, for example, AI has demonstrated promising results in the early detection of lesions, in some cases outperforming existing diagnostic evaluations (Islam et al., 2026). AI also enables more personalised treatment by analysing patient-specific data to support tailored clinical decisions (Fahim et al., 2025).

WEARABLES AND SENSORS

Wearables and sensors extend precision medicine beyond the clinical setting by gathering health data in real-world conditions. Devices such as smartwatches, continuous glucose monitors, cardiac rhythm patches, sleep trackers and movement sensors allow clinicians to collect information outside of infrequent clinic visits. This is important because many conditions change over time, and short clinical snapshots may not capture fluctuations in symptoms or physiological function. Real-time data on heart rate, activity, sleep, glucose levels and cardiac rhythm can provide a continuous and personalised picture of a patient’s health (Dinh-Le et al., 2019; Saeed et al., 2021).

Wearables are particularly valuable for monitoring chronic conditions and identifying deterioration early. In precision medicine, treatment decisions should ideally be grounded in how a patient is doing day to day, rather than solely at clinical visits. These devices can identify patterns signalling worsening status, treatment response or emerging risk before significant events occur. Their value is amplified when data feeds into EHR systems, allowing clinicians to view sensor data alongside medical history, test results and treatment plans (Dinh-Le et al., 2019).

Despite their promise, wearables face barriers to adoption in precision medicine: not all consumer devices are validated to clinical accuracy standards; patient adherence affects data quality; integrating high-volume continuous data into clinical workflows is operationally challenging without appropriate data management infrastructure; and data security and interoperability remain ongoing concerns. Nevertheless, wearables represent an increasingly important source of patient-centred, longitudinal data that conventional health systems cannot easily replicate (Dinh-Le et al., 2019; Saeed et al., 2021).

ORGAN-ON-A-CHIP TECHNOLOGY

Organ-on-a-chip devices are microfluidic systems that replicate the microarchitecture and function of human organs or tissues. They contain miniaturised chambers lined with living cells arranged to mimic organ-level physiology, enabling scientists to study how the human body responds to drugs and environmental changes (Nunes et al., 2025; Paloschi et al., 2021). These devices have been developed for many organ systems including the lung, brain and heart (Li et al., 2023). One example is their use in cardiovascular research, where patient-derived cells have been used to model thromboinflammation and compare inflammatory responses between healthy and diseased states (Sakthivel et al., 2026).

A key advantage of organ-on-a-chip technology is that it may provide more accurate drug response predictions than conventional in vitro methods, reducing reliance on animal testing, particularly for toxicity screening. These platforms can screen thousands of compounds simultaneously (Franzen et al., 2019). The average cost of developing a new drug exceeds $1.7 billion, and organ-on-a-chip technology may reduce research and development costs by approximately 10-26% by accelerating and improving the efficiency of drug testing (Zhu et al., 2020; Li et al., 2023). Some devices can maintain barrier function for several weeks, allowing longitudinal study of disease progression (Sakthivel et al., 2026).

A key limitation of organ-on-a-chip platforms in the context of precision medicine is that they do not capture inter-individual genetic and biological variation by default. Without patient-specific cells, results may not predict how a particular individual will respond. A treatment that performs well in a generic chip model may be less effective or produce different adverse effects in a genetically distinct patient, increasing the risk of suboptimal therapy (Li et al., 2023).

NEXT-GENERATION SEQUENCING

Next-generation sequencing (NGS) refers to high-throughput DNA sequencing technology that vastly surpasses earlier methods in both speed and scale (Behjati & Tarpey, 2013). Within NGS, millions of genome fragments are sequenced simultaneously and then assembled by mapping each fragment to a reference genome, enabling rapid and comprehensive genomic analysis (Behjati & Tarpey, 2013).

NGS has substantial applications in precision medicine. Where earlier methods such as Sanger sequencing required specialised workflows to detect specific abnormalities, NGS can identify a broad range of mutations — including microdeletions, insertions, fusions and copy number variants – within a single assay, though limitations remain in regions with extreme GC content or repetitive sequences (Behjati & Tarpey, 2013). In oncology, the ability to detect genomic abnormalities more comprehensively and efficiently has enabled important advances, including the identification of genotypic cancer subtypes that guide targeted treatment selection (Popova & Carabetta, 2024).

SINGLE-CELL SEQUENCING

Single-cell sequencing (SCS) involves sequencing the genome of individual cells rather than bulk tissue, providing resolution that bulk methods cannot achieve (Misra et al., 2022). While SCS applies fundamental NGS principles, it requires an additional critical step of single-cell isolation before sequencing can occur (Misra et al., 2022).

SCS has considerable potential in precision medicine, particularly in oncology. Because key driver mutations can be present in only a small proportion of tumour cells, SCS is essential for characterising the genomic heterogeneity of both primary tumours and metastatic sites, and for understanding the oncogenic cascades these mutations initiate (Misra et al., 2022). SCS also enhances understanding of the diverse cell populations that comprise complex cancer biology, enabling more precisely targeted treatment strategies (Wiedmeier et al., 2019).

MULTI-OMICS

Multi-omics is a powerful approach in precision medicine that provides a deeper and more complete understanding of disease biology than any single data type alone. Rather than examining one layer of biological information in isolation, multi-omics integrates data from genomics, transcriptomics, proteomics, metabolomics and epigenomics to study how diseases develop and differ between individuals. This is important because many diseases cannot be fully explained by genomic data alone, and combining multiple biological layers allows scientists to identify more accurate disease biomarkers, understand disease mechanisms more comprehensively and better stratify patients who may respond differently to treatment (Biemar et al., 2018; Zhou et al., 2023).

In cancer, rare diseases and chronic conditions, multi-omics can reveal molecular signatures that single-modality analysis would miss, enabling patient grouping based on biological processes rather than clinical phenotype alone. This supports earlier diagnosis, more accurate risk prediction and better treatment matching (Huang et al., 2021; Zhou et al., 2023).

Multi-omics also faces significant challenges to clinical implementation. Generating and analysing multiple data types is computationally intensive and costly, and the outputs are often difficult to translate directly into clinical decision-making. Different omics platforms produce data of varying scale and quality, complicating integration. Multi-omics also requires robust linkage with EHRs and longitudinal outcome data to reach its full potential (Biemar et al., 2018; Huang et al., 2021; Song et al., 2024).

SYNTHETIC BIOLOGY AND GENE EDITING

Synthetic biology involves the design and engineering of biological components and systems, including the targeted rewriting of DNA sequences. Gene editing is a core application of synthetic biology, enabling scientists to introduce precise changes into the genome of living cells. One clinical application is correcting pathogenic mutations in patient cells to treat genetic diseases (Kitada et al., 2018).

The most widely used gene editing platform is CRISPR-Cas9. CRISPR uses a guide RNA to locate a specific DNA sequence, directing the Cas9 endonuclease to cut at that site. Scientists can then remove, replace or modify the targeted DNA sequence (Savić & Schwank, 2016). CRISPR-Cas9 is also a powerful tool for functional genomics, allowing researchers to systematically investigate gene function (Kumlehn et al., 2018).

Gene editing also has applications in agriculture: for example, modifying crops to reduce susceptibility to parasitic plants such as witchweed, or improving yield and disease resistance through targeted trait selection (Melillo, 2017; Pixley et al., 2019).

However, the technology raises significant ethical concerns. These include the prospect of germline editing to select traits in embryos, the potential for a “designer baby” market driven by parental preferences for characteristics such as height or eye colour, and the question of whether gene editing should be restricted to medically necessary applications (Xu & Qi, 2019).

DIGITAL TWINNING

Digital twinning is an emerging frontier in precision medicine, involving the creation of a dynamic computational representation of an individual patient that simulates their biological processes, disease progression and responses to therapy (Venkatesh et al., 2022). Unlike conventional predictive models, a medical digital twin is designed to remain synchronised with its physical counterpart through continuous integration of patient-specific genomic, transcriptomic, proteomic and metabolomic data, combined with EHR data, medical imaging, physiological measurements, environmental exposures and biosensor outputs. These integrated datasets are then analysed using AI and mechanistic mathematical models to generate a high-fidelity representation of patient physiology (Katsoulakis et al., 2024). A 2024 scoping review identified 220 publications on digital twins in healthcare, demonstrating substantial and growing scientific interest (Katsoulakis et al., 2024).

In oncology, a digital twin could incorporate tumour genomic mutations, gene expression patterns and radiological characteristics to model tumour evolution and estimate resistance to specific therapies (Katsoulakis et al., 2024). Evidence for the potential of this approach comes from the VICTRE study, which used 2,986 synthetic virtual patients to compare digital mammography with digital breast tomosynthesis, with findings corroborated by a conventional clinical study of 400 women (Katsoulakis et al., 2024). In metabolic medicine, glucose measurements combined with genomic and physiological data have been used to model patient-specific glycaemic responses (Katsoulakis et al., 2024).

Limitations include the reliance of digital twins on rigorous validation and uncertainty quantification, the nonlinear complexity of biological systems and the risk that incomplete datasets or algorithmic bias could generate misleading predictions. Additional challenges include computational requirements, interoperability between healthcare databases, patient privacy and regulatory responsibility for algorithm-supported clinical decisions (Venkatesh et al., 2022; Katsoulakis et al., 2024).

Applications and Evidence of Precision Medicine

CANCER

Cancerous tumours are genetically and molecularly heterogeneous. Even tumours arising in the same organ can have entirely different molecular characteristics, meaning that patients with the same cancer type may respond very differently to the same treatment. Precision oncology uses biomarker and molecular testing to identify tumour-specific abnormalities, enabling treatment selection targeted at those abnormalities.

A well-established example is HER2-positive breast cancer. In certain breast cancers, the HER2 protein is overexpressed, making it a targetable biomarker. HER2 overexpression is associated with aggressive tumour growth; identifying it allows direct targeting and interruption of the growth signal. A landmark trial demonstrated that adding trastuzumab – a monoclonal antibody specifically targeting HER2 – to chemotherapy significantly improved clinical outcomes in patients with HER2-overexpressing metastatic breast cancer (Slamon et al., 2001). This illustrates how identifying a specific biomarker can guide selection of targeted treatments that improve outcomes.

Precision medicine has also transformed treatment of non-small-cell lung cancer (NSCLC). Patients whose tumours carry EGFR mutations respond to EGFR-targeted therapies. In the FLAURA trial, patients with EGFR-mutated advanced NSCLC treated with osimertinib achieved a median progression-free survival of 18.9 months, compared with 10.2 months in patients receiving standard EGFR inhibitors (Soria et al., 2018), establishing how molecular characterisation can guide treatment selection and improve clinical outcomes.

Genomic sequencing can identify multiple molecular abnormalities simultaneously. The I-PREDICT study used comprehensive genomic profiling to match patients with advanced cancers to personalised treatment combinations. Patients with higher molecular matching scores had better disease control and longer progression-free and overall survival, demonstrating an association between specific treatment matching and improved outcomes (Sicklick et al., 2019). However, as I-PREDICT was not a randomised controlled trial, these findings represent an association rather than proof of causation.

Precision oncology also has important limitations. The NCI-MATCH trial showed that identifying a molecular abnormality does not always mean an effective targeted treatment exists for it (O’Dwyer et al., 2023). Tumour heterogeneity, the development of drug resistance and the absence of actionable mutations can substantially limit the benefits of precision approaches. Overall, precision medicine plays a strong role in treatment selection, but benefits vary considerably depending on the molecular characteristics of each tumour.

DIABETES

Diabetes is a chronic condition characterised by impaired blood glucose regulation. Precision medicine in diabetes aims to improve diagnosis and treatment by recognising that diabetes is not a single biologically uniform condition. Factors including genetic background, insulin production, insulin resistance, medication response and complication risk differ substantially between patients. The Precision Medicine in Diabetes Initiative (PMDI) describes precision diabetes medicine as an approach that may improve diagnosis, prevention, treatment and prognosis using individual biological and clinical characteristics (Tobias et al., 2023).

The clearest current clinical application of precision diabetes medicine is genetic testing for monogenic diabetes, in which diabetes results from a pathogenic variant in a single gene. Because its symptoms closely resemble those of type 1 or type 2 diabetes, patients may be incorrectly classified without genetic testing. Identifying the underlying mutation changes both the diagnosis and the treatment approach. For example, patients with HNF1A-MODY – a form of maturity-onset diabetes of the young – show particular sensitivity to sulfonylureas, enabling a switch from insulin injections to oral medication (Raile et al., 2015). This demonstrates how the biological cause of diabetes can substantially influence treatment selection.

Researchers have also begun investigating whether common type 2 diabetes can be divided into more biologically meaningful subgroups. Ahlqvist et al. (2018) analysed patient clusters based on age at diagnosis, BMI, HbA1c, insulin resistance, beta-cell function and autoimmune markers, identifying five adult-onset diabetes clusters that differed in disease progression and complication risk. For instance, the severe insulin-resistant cluster showed a markedly higher risk of diabetic kidney disease, suggesting that cluster membership could guide targeted surveillance and treatment.

The precision medicine approach to common type 2 diabetes remains under development. A systematic review found that although reproducible diabetes subgroups can be identified, evidence that assigning treatments according to cluster membership improves patient outcomes remains insufficient (Misra et al., 2023). While precision medicine is clinically valuable for genetically defined forms of diabetes such as MODY, broader application to type 2 diabetes is a promising but still emerging prospect.

BATTEN DISEASE

Batten disease is a group of rare inherited neurodegenerative disorders caused by mutations in different genes, affecting the nervous system and causing seizures, vision loss, movement problems and progressive cognitive decline. Its genetic heterogeneity makes it a compelling example of why personalised medicine can be important: patients with different mutations may not respond to the same treatment.

A significant example is milasen, a personalised antisense oligonucleotide therapy developed for a single child (Mila) with CLN7 Batten disease caused by a unique mutation in the MFSD8 gene that disrupted RNA splicing (Kim et al., 2019). Researchers used the patient’s specific genetic information to design a molecule that would correct the aberrant splicing. Before clinical administration, milasen was tested on patient-derived cells and shown to improve splicing, providing preclinical evidence of efficacy. The treatment was developed and administered within approximately one year – a remarkable timeline given that conventional drug development typically takes many years. Following treatment, Mila experienced a reduction in seizure frequency and duration, although the single-patient design precluded formal efficacy evaluation by conventional clinical trial standards (Kim et al., 2019).

The milasen case demonstrates how genetic sequencing and molecular biology can enable treatment to move from a one-size-fits-all approach towards therapies designed around the individual patient. It also highlights the key challenges of personalised treatments: high development costs, technical complexity and the difficulty of scaling bespoke therapies to larger patient populations.

PHARMACOGENOMICS

Pharmacogenomics (PGx) is increasingly used as a clinical decision-support tool to guide medication selection, dosing and monitoring. Evidence suggests that PGx is most effective when integrated with professional clinical judgement rather than applied autonomously. Genetic variation can identify patients with altered drug metabolism, reduced therapeutic response or increased risk of adverse drug reactions. However, medication response is also influenced by age, renal and hepatic function, comorbidities and drug-drug interactions (FDA, 2022; Swen et al., 2023). The FDA recognises pharmacogenetic associations but emphasises that an association does not necessarily mean testing is required before prescribing unless specified by drug labelling or a companion diagnostic (FDA, 2022). CPIC guidelines primarily support clinical use of existing genetic results rather than determining who should be tested (Caudle et al., 2014).

PGx has the strongest clinical evidence for established gene-drug pairs, where genetic variation has a well-characterised effect on drug response and a clear prescribing consequence. Key examples include HLA-B and abacavir, DPYD and fluoropyrimidines, TPMT/NUDT15 and thiopurines, and CYP2C19 and clopidogrel (FDA, 2022; Amstutz et al., 2018). For example, HLA-B*57:01 is strongly associated with abacavir hypersensitivity, providing a clear basis for avoiding abacavir in carriers (FDA, 2022). Reduced DPYD activity increases the risk of severe fluoropyrimidine toxicity, supporting CPIC-recommended dose reduction or treatment avoidance (Amstutz et al., 2018). Conversely, the presence of a genetic association alone does not necessarily justify testing or alter treatment, reinforcing the need for PGx results to be interpreted within the wider clinical context.

Evidence from prospective studies supports PGx where gene-drug relationships are well established. In the PREPARE study (n = 6,944), clinically relevant adverse drug reactions occurred in 21.5% of patients receiving genotype-guided treatment versus 28.6% receiving standard care (OR 0.70, 95% CI 0.61–0.79; p < 0.0001) (Swen et al., 2023). These findings indicate that PGx can provide a clinically meaningful safety benefit, though they do not support treating PGx as universally beneficial across all medications or genetic variants.

Evidence for PGx improving treatment efficacy is less consistent. A systematic review and meta-analysis reported greater symptom reduction after eight weeks with PGx-guided antidepressant treatment, although substantial heterogeneity remained (Milosavljević et al., 2024). In TAILOR-PCI, genotype-guided treatment reduced ischaemic events among CYP2C19 loss-of-function carriers, but the primary comparison did not reach conventional statistical significance (HR 0.66, 95% CI 0.43–1.02; p = 0.06) (Lee et al., 2020).

Successful PGx implementation requires integration with clinical decision-making, combining validated genetic results with medication history, organ function, comorbidities and drug interactions within EHR systems (Caudle et al., 2014). Challenges include clinician knowledge, patient understanding, cost, reimbursement, workflow integration and uncertainty about which gene-drug pairs to prioritise (Arbitrio et al., 2025; Wu et al., 2025). Differences in genetic ancestry and underrepresentation in pharmacogenomic research may affect the generalisability of genotype-phenotype relationships across populations (Fricke-Galindo & Lazalde-Ramos, 2022; Pirmohamed, 2023). Overall, PGx should be viewed as a clinical decision-support tool whose greatest value lies in selectively applying robustly validated gene-drug associations alongside conventional clinical assessment (FDA, 2022; Caudle et al., 2014; Wu et al., 2025).

CASE STUDIES

One prominent example of precision oncology in practice is the National Cancer Institute’s Molecular Analysis for Therapy Choice (NCI-MATCH) trial. Rather than classifying treatment solely by the anatomical origin of a malignancy, NCI-MATCH investigated whether patients could receive targeted therapies based on the molecular abnormalities driving their cancers, regardless of tumour site. The trial enrolled 1,201 patients across 38 treatment arms (NCI, n.d.).

One NCI-MATCH arm investigated AZD4547, an inhibitor targeting fibroblast growth factor receptor (FGFR) signalling. Of 50 patients with FGFR pathway alterations who received AZD4547, approximately 10% demonstrated a partial response, with responding tumours arising from diverse anatomical sites — two carrying FGFR2/3 mutations and two FGFR3 fusions (NCI, 2018). This illustrates a central principle of precision oncology: molecular abnormalities shared across anatomically distinct tumours may provide therapeutically actionable targets.

In pharmacogenomics, the interaction between clopidogrel and CYP2C19 is a well-established clinical example. CYP2C19 is required for the bioactivation of clopidogrel into its pharmacologically active metabolite. Genetic polymorphisms produce substantial inter-individual variability in CYP2C19 activity; individuals classified as intermediate or poor metabolisers show reduced active metabolite formation, decreased platelet inhibition and an elevated risk of major adverse cardiovascular events (Lee et al., 2022). Clinical guidelines therefore recommend alternative antiplatelet agents — including prasugrel or ticagrelor — for CYP2C19 intermediate and poor metabolisers when clinically appropriate (Lee et al., 2022).

A further landmark application is the treatment of spinal muscular atrophy (SMA), caused by homozygous deletion or disruption of the SMN1 gene. SMN2, a highly homologous paralogous gene, predominantly produces transcripts lacking exon 7 and therefore insufficient functional SMN protein (Mercuri et al., 2018). Nusinersen, an antisense oligonucleotide, modifies SMN2 pre-mRNA splicing to increase exon 7 inclusion and thereby enhance functional SMN protein production. In the randomised ENDEAR trial, 41% of infants receiving nusinersen achieved a motor-milestone response compared with 0% in the control group, leading to early termination of the trial due to treatment efficacy (Finkel et al., 2017). This provides particularly powerful evidence for precision medicine: therapeutic design exploited detailed knowledge of the molecular mechanism underlying the patient’s genetic disease.

Challenges

While precision medicine presents as a promising emerging area of medicine, there are considerable challenges that must be acknowledged. One major concern is equity of access. Precision medicine treatments, such as gene therapies, are among the most expensive interventions in modern healthcare. In privatised healthcare systems, economically advantaged individuals and systems are better placed to access them (MacEachern & Forkert, 2021). Most targeted therapies cost in excess of $400,000 per treatment course, and many run into the millions (Subica, 2023). This creates a substantial access disparity between patients who can and cannot afford these treatments.

Another challenge concerns the relationship between precision medicine and health equity. Research has shown that minorities in the United States receive lower quality healthcare independent of access factors (Smedley et al., 2003). Precision medicine has the potential to address some of these disparities by providing more individually accurate diagnoses and treatments. However, critics have argued that race-based differences in disease risk are unscientific and driven by systemic factors rather than biology – and that embedding racial categories into precision medicine frameworks risks perpetuating unfounded assumptions about biological differences between racial groups (Matthew, 2019; Ramsoondar et al., 2023).

The collection of large volumes of individual patient data, enabled by new sensor and sequencing technologies, also raises significant data privacy concerns. Patient data can be shared with or sold to third parties, and researchers may use private patient data without individual consent (Klonoff et al., 2026). Machine learning models used to synthesise multi-omics data can produce false positives and analytical errors, potentially contributing to misdiagnoses.

Finally, although AI integration into precision medicine offers substantial benefits, key limitations must be considered. AI outputs can be biased when training data is not representative, leading to systematically worse predictions or recommendations for certain patient groups (Johnson et al., 2021). In particular, models trained on biased data may fail to account for health risks in underrepresented populations (Hartl et al., 2021). Ensuring that training datasets are unbiased, diverse and of high quality is therefore essential. Furthermore, as machine learning models grow in complexity, they become increasingly difficult for clinicians to interpret: their internal decision-making processes are opaque even when inputs and outputs are visible. This “black-box” problem can erode confidence in model-driven recommendations and introduce uncertainty into patient care (Hartl et al., 2021). Approaches such as explainable AI (XAI), Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) have been developed to address this by revealing the pathways and feature contributions underlying model outputs, though their application in clinical practice remains in early stages (Fahim et al., 2025).

Ethics

Precision medicine relies on biological and genomic datasets, but its ethical implications extend far beyond biology to questions of representation, cost, autonomy, confidentiality and clinical decision-making. Two central equity concerns affect precision medicine at different stages of its development pathway. Ancestral underrepresentation in genomic research limits the accuracy of PM tools for non-European populations (Lee et al., 2022b), while high intervention costs limit who can access its benefits after development (University of Copenhagen, 2023).

The scale of the representation problem is illustrated by several examples. The UK Biobank contains over 95% European-ancestry participants. In a real-world type II diabetes application across ten healthcare institutions involving 55,029 participants, the dataset comprised 73% White, 20% Black/African American and fewer than 1% Asian participants – making the Asian-ancestry dataset too small for analysis (Li et al., 2021). Approximately 96% of genome-wide association study (GWAS) participants have been of European ancestry (Li et al., 2021). Models built predominantly on European-ancestry data may therefore produce less accurate predictions for non-European populations, generating unequal health benefits. Federated Transfer Learning (FTL) has been proposed as a potential remedy: training AI models on diverse, globally distributed genomic datasets to ensure tools work accurately for a broader range of populations (Li et al., 2021).

Regarding cost, trastuzumab for HER2-positive breast cancer has an incremental cost-effectiveness ratio (ICER) of approximately $62,000 per quality-adjusted life year (QALY) — within the $50,000–$100,000/QALY threshold considered cost-effective in high-income settings (University of Copenhagen, 2023). However, even this “cost-effective” treatment places a heavy burden on low-income healthcare systems and families. CAR-T cell therapies have ICERs of approximately $373,000–$475,000 per QALY, making them economically prohibitive even in many high-income settings and effectively inaccessible in low- and middle-income countries (Shah, 2024). This creates a profound accessibility divide in potentially life-saving treatments.

Both equity concerns – representation and cost – converge on the same conclusion: precision medicine as currently developed risks reproducing and amplifying existing health inequalities. They differ in which stage of the PM pathway they implicate, with the representation concern arising at the level of evidence generation and the cost concern arising at the point of clinical access.

A further major ethical issue is patient autonomy and confidentiality. The rapid evolution and complexity of precision medicine creates challenges in ensuring that patients are fully informed before consenting to treatment, and that their genetic information remains secure after it has been shared (Parker, 2024).

Regarding confidentiality, genetic information cannot always be made truly anonymous. Even after superficial identifiers such as name and medical history are removed, individuals may be re-identified through familial relationships, cross-referencing and increasingly sophisticated data analysis. As biobanks grow and between 100 million and 1 billion genomes are sequenced globally, re-identification risks increase despite de-identification efforts (Rubio, 2025).

Regarding informed consent, particularly in precision oncology, patients may face highly complex, uncertain and emotionally difficult diagnostic and treatment decisions. Greater complexity and uncertainty can impair the ability to make fully informed decisions (Greely, 2026). Both confidentiality and consent challenges reflect that patient autonomy can be threatened at multiple stages of the healthcare system.

Despite these concerns, it would be oversimplistic to characterise precision medicine as simply unethical. PM is not inherently unethical, but how its benefits, costs and risks are distributed may be. Data security risks are not unavoidable; however, strategies including encryption, secure data management systems and pseudonymisation can meaningfully mitigate re-identification risks (Rubio, 2025). Cost-effectiveness depends heavily on healthcare context. The critical task is ensuring that the development and deployment of precision medicine is governed by frameworks that protect patient autonomy, promote equitable access and maintain trust across diverse communities.

Conclusion

Precision medicine has the potential to fundamentally change how healthcare is delivered by moving away from population-averaged treatments towards approaches that are tailored to the individual characteristics of each patient. As this article has demonstrated, differences in genes, molecular biology and other patient-specific factors can substantially affect how diseases develop and how patients respond to treatment. By systematically incorporating this information, precision medicine can support better decisions about prevention, diagnosis and treatment.

Technology is foundational to this shift. Tools including genomic sequencing, multi-omics platforms, EHRs, AI and wearable devices collectively enable the collection, integration and analysis of patient-specific data at a scale and resolution previously impossible. Together, they provide a richer picture of individual patients and their disease biology than any single technology can achieve. However, challenges including high costs, large data volumes and difficulties in integrating information across systems remain significant barriers.

The clinical applications reviewed in this article demonstrate that precision medicine can deliver meaningful patient benefit. In cancer treatment, genomic and molecular testing has enabled targeted therapies – as in HER2-positive breast cancer and EGFR-mutated lung cancer – that have substantially improved outcomes (Slamon et al., 2001; Soria et al., 2018). Pharmacogenomics offers a clinically validated route to safer and more individualised prescribing (Swen et al., 2023). For rare diseases, as illustrated by milasen, genetic sequencing and molecular biology can enable therapies designed around a single patient’s mutation (Kim et al., 2019). Precision medicine is most valuable when specific patient information can be directly connected to a treatment decision.

However, the development of precision medicine also brings important societal challenges. Equitable access is a fundamental concern: genetic testing and personalised treatments are expensive, and populations underrepresented in genomic research may derive less benefit from PM tools calibrated primarily on European-ancestry datasets (Lee et al., 2022b; Matthew, 2019). Addressing this requires both investment in diverse research cohorts and policy mechanisms to widen affordability.

Privacy, data governance and AI bias are also pressing issues. Precision medicine depends on large-scale personal health and genomic data, raising concerns about how this information is stored, shared and used. AI can greatly assist in data analysis but must be used to support, rather than replace, clinical judgement, and must be built on representative, unbiased training data (Johnson et al., 2021). Clear governance frameworks and responsible data stewardship will be essential for maintaining public trust.

In summary, precision medicine is a genuinely promising approach with the potential to make healthcare more effective, individualised and preventive. Its success will depend not only on continued scientific and technological advance, but on ensuring affordable access, diverse research, robust data protection and responsible use of emerging technologies. Precision medicine should be seen not as a perfect solution to every medical problem, but as a framework for giving clinicians and patients more useful, targeted information when making consequential decisions about individual health.

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