Review Article
Integrated precision medicine and artificial intelligence in healthcare
V. Jeypal, T. Cherian
Hridayalaya Heart Foundation and Artificial Intelligence, Thiruvananthapuram, Kerala, India
Corresponding author: V. Jeypal, Email: jeyapalv57@gmail.com
Journal of Experimental Biology and Zoological Studies. 2(1): p 29-44, Jan-Jun 2026.
Received: 01/12/2025; Revised: 20/12/2025; Accepted: 22/12/2025; Published: 01/01/2026
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Abstract
The current healthcare landscape is undergoing a transformative revolution driven by the synergistic
integration of precision medicine, artificial intelligence and robotics. This convergence is
fundamentally shifting the paradigm from standardized treatments to highly individualized patient
care, promising unprecedented improvements in diagnostic accuracy, therapeutic efficacy, and
operational efficiency. Artificial Intelligence enables the rapid analysis of massive, multimodal
datasets, including genomics, proteomics, electronic health records and real-time physiological data
from wearable sensors. Robot-assisted surgery offers surgeons enhanced competency, tremor
filtration, and high-definition 3D visualization, leading to minimally invasive procedures and fewer
complications and faster patient recovery. This review highlights the current state, transformative
impact, and future trajectory of combining precision medicine with AI and robotics, emphasizing its
potential to deliver safer, more efficient, and truly personalized healthcare for all.
Keywords: Artificial intelligence, biomarkers, biosensors, epigenomics, precision medicine,
telemedicine.
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Introduction
The field of medical treatment has undergone a profound paradigm shift in recent decades, driven
by revolutionary advancements in diagnostic and therapeutic technologies. This evolution has
fundamentally altered conventional medical practice, transitioning the focus from a standardized,
“one-size-fits-all” model to a highly personalized approach to patient care. The continuous
integration of digital technology across all levels of the healthcare advancement is the core catalyst
for this transformation. This shift has enabled healthcare to advance at the individual patient level,
supporting the realization of precision medicine. Artificial intelligence (AI) serves as a critical
engine for this modern approach. AI systems are capable of analysing vast and diverse sets of
medical data, including, Medical Imaging: X-rays, magnetic resonance imaging (MRI) scans,
computer tomography (CT) scans, and ultrasounds; bio-signals: electrocardiogram (ECG),
electroencephalogram (EEG), and electromyography (EMG); Clinical Data: Electronic health
records (EHRs), vital signs, patient history, and laboratory results. This sophisticated data analysis
empowers medical professionals to diagnose illnesses with greater efficiency and accuracy.
Crucially, the automation of complex data processing reduces the workload on medical
practitioners, allowing them to redirect their expertise and time towards direct patient engagement
and care. Ultimately, AI-driven data analysis is foundational to delivering precision medicine,
enabling the prediction of outcomes, and accurately modelling the progression of diseases.[1] This
review aids in decision‐making and provides precise predictive outcomes, helping healthcare
professionals make better‐informed choices regarding patient care. It is useful in the Emergency
Department for physicians to focus on difficult cases. It is also useful in rural medical settings
where physicians are less available.
Precision medicine: The paradigm shift
“Precision medicine”, previously referred to as personalised medicine, represents a revolutionary
shift from traditional approaches toward truly individualized therapy. The rationale behind
precision medicine is that, since no two individuals are the same, their healthcare should be tailored
accordingly. Instead of treating diseases generically, precision medicine guides treatment
decisions using an individual’s genomic, environmental, and lifestyle information.[2] Thus, the
treatment is no longer guided by the traditional model in which the physician is considered the
sole authority, being evolved into a structured, integrated, and collaborative model (Figure 1). The
goal of precision medicine is to provide a more precise approach for the prevention, diagnosis and
treatment of disease.
Figure 1: Comparison between traditional healthcare practices and modern healthcare practices.
The tools employed in precision medicine include AI, various omics technologies, environmental
factors, and social determinants. Information derived from these sources is then integrated with
preventive and population-level medical approaches. However, precision medicine primarily relies
on data from EHRs, which incorporate clinical findings, outputs from biomarkers, laboratory test
results, and radiological reports.[3] Analysing this vast amount of information requires machine-
learning techniques that use complex decision pathways and algorithms to guide disease
management.[4] Thus, the traditional “one-size-fits-all” approach is giving way to precision-based
treatment strategies. Environmental, social, and behavioural factors also hold great significance in
precision medicine, especially in the management of complex diseases, where treatment success
depends heavily on addressing these determinants. Ultimately, integrating precision approaches
with preventive and public health strategies creates a comprehensive framework that is poised to
transform the practice of medicine in the future.
The cosmic to cellular journey of precision medicine
The Cosmic-to-Cellular (C–C) journey, in the context of precision medicine and holistic health, is
an emerging philosophical and interdisciplinary perspective that explores how large-scale natural
phenomena are related to microscopic biological processes. This viewpoint does not assert direct
causation but considers how environmental factors such as light, temperature, and naturally
occurring electromagnetic (EM) fields may influence human physiology through established
biophysical mechanisms. It is perceived as an idea that links the "cosmic" environment directly to
the "cellular" health.[5] While the suggested long-term interactions between the cosmic
environment and cellular health remains hypothetical, research does show that EM fields can
influence certain cellular functions under specific conditions in vitro or in controlled environments.
For example, EM fields have been studied for their potential effects on cell proliferation, apoptosis,
and tissue repair,[6] though these findings are context-dependent and not yet definitive for clinical
practice. The C-C journey to health may be regarded as an aspiration to create a medical model
that uses cellular-level precision medicine to diagnose and treat diseases in a holistic way that
includes a person's genetics, environment, and lifestyle.
The “Cosmic” aspect aims to place cellular biology within a broader physical and universal
context, drawing conceptual connections between physics, electromagnetism, and life processes.
The universe is governed by fundamental physical laws, including Einstein’s mass energy
equivalence (E = mc²), which expresses the inherent relationship between matter and energy. They
underscore that the energy transformations driving cellular processes operate within the same
universal physical framework that governs all matter and energy. Since cellular processes and
metabolic activities are energy-dependent, diseases can sometimes be detected or quantified
through alterations in molecular energetics. Presently, using quantum-based strategies to correct
diseases remain a theoretical idea rather than an established approach. Nevertheless, quantum-
level insights continue to support the development of advanced diagnostic technologies and future
therapeutic innovations.[7]
Application of Quantum theory and mechanics in medicine
Quantum medicine refers to emerging research that applies principles of quantum theory and
quantum mechanics to better understand biological processes at the molecular and subatomic level;
areas where classical physics offers limited explanatory power. Quantum theory describes the
behaviour of matter and energy at the atomic and subatomic scales. Instead of describing particles
with fixed paths, it uses wave functions, quantized energy levels, and probabilities to predict where
particles might be and how they behave. Although the biological processes themselves are
governed by large-scale quantum effects, most cellular functions are described by classical
biochemical principles. However, its direct application to medicine occurs mainly through
technologies derived from quantum principles.[7] For example, quantum physics enables medical
imaging techniques such as MRI which relies on nuclear spin resonance, laser-based surgical tools,
and semiconductor devices used in diagnostic equipment. Quantum chemistry also supports our
understanding of molecular interactions relevant to DNA damage, enzyme function, and protein
structure. For example, at the molecular level, quantum chemistry helps explain phenomena such
as electron transfer in cellular respiration, the photochemical reactions in DNA viz., UV-induced
mutagenesis, and the energetics governing protein folding and molecular binding interactions. [7,8]
Emerging fields such as quantum biology explore whether quantum effects like tunnelling and
coherence play roles in specific biological events like enzyme catalysis or photosynthesis.
Quantum tunnelling is particularly important in biology for reactions involving the transfer of light
particles, primarily electrons and hydrogen (as protons, hydrogen atoms, or hydride ions).[8,9] It is
a process where a particle, such as a proton or electron, can pass through an energy barrier even if
it does not have enough classical energy to overcome it. This is being investigated in areas like
enzyme catalysis and spontaneous mutations in DNA. A specific example is alcohol
dehydrogenase which shows hydrogen tunnelling during its catalysis of alcohol oxidation.[10]
Quantum coherence refers to the existence of a superposition of quantum states or a constant,
phase-locked wave function that can lead to interference effects. This is primarily studied for its
potential role in efficient energy transfer during photosynthesis. [8,11] While quantum biology has
increasing empirical support in some biological processes (e.g., photosynthesis), proposals that
quantum theory provides a direct explanation for mental states or the hard problem of
consciousness are part of ongoing, often controversial, philosophical and theoretical debates, and
are not considered established scientific facts.[12] In medicine, quantum-based technologies
continue to advance diagnostics and treatment. Techniques derived from quantum physics have
improved early disease detection, increased the precision of imaging systems, and contributed to
research in neurological disorders. [7,13] These advances stem from quantum-enabled
instrumentation and computational modelling, rather than from direct manipulation of quantum
states in living systems.
Genomics versus epigenomics
Genotype‐guided treatment is one of the most extensively researched applications of precision
medicine in healthcare today, helping clinicians determine the appropriate dosage based on
genotype information in the 23 pairs of chromosomes.[14] The human genome is composed of
roughly 6 billion DNA base pairs that contain all the code needed to create a human being.
Personalized medicine customize treatment based on individual patient data, such as genomic and
biochemical information due to individual variations. Advances in technologies like DNA
sequencing and proteomics have highlighted the need for this approach. Future challenges include
enhancing the efficiency of patient characterization and developing effective personalized
treatments, although universally effective drugs may still be sought; yet remain difficult to
achieve. For patients with lung or breast cancer, genomic profiling of malignancies can inform
customized treatment regimens. Incorporating precision medicine into healthcare can lead to more
accurate diagnoses, enable the early identification of ‘at-risk patients’ before symptoms appear,
and provide individualized treatment plans that balance effectiveness and safety.[15] Another
application includes assessment of potential responses to new drugs, predicting disease prognosis.
Epigenomic-guided treatment utilises the study of how environmental factors affect gene
expression, without altering the underlying DNA sequence, to develop targeted therapies.
Epigenomics examines the non-coding regions and modifications therein that control gene
expression without manipulating the DNA sequence itself. These include the following methods:
DNA methylation, Histone modifications, and Lifestyle-based genetic modifications.[16]
DNA methylation
DNA methylation is a biological process by which methyl groups are added to the DNA molecule.
It is a normal and vital epigenetic function in eukaryotic organisms that generally acts to switch
off or repress gene function. Methylation can change the activity of a DNA segment without
changing the sequence. When located in a gene promoter, DNA methylation typically acts to
repress gene transcription. In mammals, DNA methylation is essential for normal development
and is associated with a few key processes including genomic imprinting, X- chromosome
inactivation, and repression of transposable elements. It also has significant impacts on aging and
carcinogenesis.[17] The nucleobases on which natural, enzymatic DNA methylation takes place are
adenine and cytosine (Figure 2). The modified bases are N6-methyladenine, 5-methylcytosine.
Figure 2: Unmodified and methylated forms of adenine and cytosine
The abnormal DNA methylation landscape in cancer is characterized by a widespread loss of
methylation, which can lead to chromosomal instability or oncogene activation. Meanwhile, site-
specific increases in methylation may silence vital tumour suppressor genes. The goal of targeted
DNA methylation therapy is to reverse these patterns, thereby restoring normal gene function and
improving the effectiveness of other treatments. If genes responsible for maintaining healthy heart
structure or keeping blood vessels flexible become hyper methylated, they get silenced. This can
lead to maladaptive cardiac remodelling, fibrosis, and reduced contractility seen in heart failure.
Conversely, following hypomethylation, a condition in which the methylation patterns are lost,
the genes that promote inflammation or cell proliferation may become overactive, hastening
plaque buildup in atherosclerosis.[18] DNA methylation acts as a crucial intermediary, translating
the impact of your environment and lifestyle (like diet, stress, and toxins) into specific changes in
gene activity within the cells. These epigenetic alterations can either silence protective genes or
activate harmful ones, directly influencing the development and progression of diseases like heart
disease. As methylation is reversible, it presents a target for future diagnostic biomarkers and
therapies.[19]
Histone modifications
The packaging of DNA begins with the fundamental double-stranded DNA helix, which has a
width of 2 nanometres (nm).[20] This DNA wraps around a cluster of histones (specifically a histone
octamer) to form nucleosomes. Each nucleosome core involves the DNA coiling, approximately
1.65 times around the octamer, creating the initial level of chromatin organization. These
chromatosomes pack together to generate the 30 nm chromatin fibre, representing a more
condensed form of chromatin. The 30 nm fibre undergoes additional folding, organizing into 300
nm loop domains which provide another level of compaction. These loops organise genes within
the nucleus. The 300 nm loops compress to 250 nm which are tightly coiled and folded into a dense
chromatid structure. This final level of packaging produces the characteristic 1400 nm thick
chromatid seen during cell division.[21]
Histones are essential, highly basic proteins that act as spools around which DNA wraps. Their
functional significance is twofold: they provide the necessary compaction to fit the genome within
the nucleus, and through chemical Post-Translational Modifications (PTMs) like acetylation and
methylation, they actively regulate gene expression, a mechanism central to normal cellular
function. Dysregulation of nucleosomes or their modifications is strongly implicated in several
diseases, particularly cancer and various developmental disorders, as such errors disrupt the
precise control over which genes are accessible and active. Histone modifications are covalent
post-translational changes that occur on the histone proteins responsible for packaging DNA into
chromatin. These modifications are a key component of epigenetic regulation. They influence
gene activity by changing the physical properties of chromatin, either making it more accessible
for transcription or more condensed or repressed. [21]
Lifestyle-based genetic modification
Epigenetic lifestyle modifications influence gene expression in individuals through external factors
by switching ‘on or off’ the genes involved. Some of the external factors include diet and nutrition,
physical activity, psychological stress and pollutants.[22]
Diet and Nutrition: Specific nutrients (like folate, B vitamins, or polyphenols) can act as co- factors
for the enzymes that perform DNA methylation or histone modification. So balanced healthy diet
is important for normal gene regulation.
Physical Activity: Exercise can induce epigenetic changes that are beneficial for metabolism and
inflammation.
Psychological Stress: Stress, especially chronic or early-life stress (adverse childhood
experiences), has been shown to significantly impact epigenetic effects.
Toxins and Pollutants: Exposure to tobacco smoke, heavy metals such as arsenic, and air pollution
can alter epigenetic patterns. For example, prolonged exposure to black carbon and SO4 particles
is found to be associated with hypomethylation of two types of repetitive elements [23] Similarly,
exposure to certain drugs or industrial chemicals can disturb the gene function.
The genetic orchestra and cellular communication
The concepts of the “genetic orchestra” and cellular communication are central to precision
medicine, moving it beyond traditional healthcare practices. This framework recognizes that
thousands of genes, proteins and signalling pathways work together in a complex system of cellular
function. The piezoelectric properties of DNA's crystal structure enable it to convert mechanical
pressure into electrical signals, creating a communication network that extends from the nucleus
to surrounding cells.[24] Thirty-seven trillion cells communicating through electrical signals
highlights the incredible complexity of our biological systems. In this analogy, disease can be
seen as a discord note in the biological orchestra, and precision medicine aims to restore harmony
by understanding the underlying molecular miscommunication. While the genetic orchestra
dictates what a cell can do, cellular communication governs how cells coordinate this activity in
real-time. Cells communicate over various distances using signal molecules (ligands) like
hormones, neurotransmitters, or growth factors that bind to specific receptors on other cells,
initiating a signal transduction cascade.[25]
Biomarkers in cancer
AI can detect cancer at molecular level. Cancer biomarkers are biological molecules, such as
genes, proteins, or other substances, that are found in tissues, blood, or other body fluids, that
provide essential diagnostic and prognostic information about a person's cancer, providing crucial
information about the status and progression of malignancy. These markers are essential to the
practice of precision medicine because they reveal a tumour’s unique biological characteristics and
molecular drivers, which vary significantly between patients, even those with the same cancer
type. It replaces the traditional treatment with high accuracy for early detection and intervention.
Treatment strategies relying on biomarker data may help to effectively combat cancer associated
conditions such as uncontrolled proliferation, genomic instability, immune evasion, and the
development of metastasis to adjacent organs. OncoMark is an AI-based computational tool
developed by Indian researchers to decode cancer at the molecular level.[26] It can guide clinicians
toward highly personalized and targeted therapies that directly inhibit cancer specific molecular
pathways.
Biosensors
A biosensor typically consists of two main components: A bioreceptor (such as an enzyme,
antibody, or DNA sequence) that specifically recognizes the target analyte and a transducer that
converts this biorecognition event into a measurable mechanical, optical, or electrochemical
signal. Early wearables primarily functioned as physical sensors, recording parameters such as
heart rate, steps walked, and calories expended to monitor general performance and health. Over
time, these devices evolved from tracking athletic activity, addressing broader healthcare needs,
including diabetes management and remote monitoring for geriatric patients. More recently,
wearable biosensors have been developed that incorporate biological recognition elements—such
as enzymes, antibodies, cell receptors, and even cellular organelles—to detect a variety of
biomarkers.[27] These advanced devices can measure analytes noninvasively through biofluids like
sweat, saliva, or interstitial fluid. Wearable biosensors offer several advantages such as low energy
consumption, high specificity, portability, affordability, rapid response, and flexibility for
comfortable long-term use. Non-invasive designs also enhance user-friendliness, reduce infection
risk, and minimize the need for invasive procedures. Some of the fascinating devices include
contact lens‐based tear monitoring, an emerging non-invasive technology where a contact lens is
designed to act as a biosensor to analyse the composition of the tear fluid,[28] colorimetric
wearables that assess sweat as an alternative biomarker for measuring blood glucose levels and
electronic nose sensors that analyse volatile organic compounds (VOC) in breath samples for non-
invasive detection of neurological disorders.[29] Oncological biomarkers like proto-oncogenes and
oncogenes have revolutionized cancer detection and treatment selection. Key advances include
liquid biopsy approaches, such as blood-based assays that detect cancer-associated biomarkers
including circulating tumour DNA (ctDNA)[30] and circulating tumour cells (CTCs). These
technologies enable non-invasive monitoring of tumour evolution, treatment response, and
minimal residual disease detection with sensitivity approaching 0.01% mutant allele frequency.
Multi-gene assays (e.g., FoundationOne, Guardant360) identify actionable mutations in genes
including EGFR, ALK, KRAS, BRAF, and HER2, directing targeted therapy selection.[31] Tumour
Mutational Burden (TMB) serves as a predictive biomarker for immunotherapy response across
multiple cancer types.
Quantum medicine explores the use of advanced biosensors to assess health and detect diseases.[27]
These quantum-enhanced biosensors employ techniques such as brain imaging,[32] single-cell
spectroscopy,[33] and non-invasive biochemical monitoring. Single-cell spectroscopy allows the
analysis of molecular changes at the level of individual cells, supporting early disease detection
and enabling more personalized treatment strategies. Similarly, quantum-based brain imaging
techniques can help estimate neuronal activity and monitor neurotransmitter dynamics with high
sensitivity. State-of-the-art imaging systems and advanced biosensors can identify subtle
alterations in metabolic substrates associated with neurological conditions such as Alzheimer’s
disease, Parkinson’s disease, and multiple sclerosis.[34] This facilitates earlier detection and can
guide the selection of targeted therapeutic interventions. In addition, genetically encoded
biosensors allow real-time observation of intracellular signalling pathways and neurotransmitter
activity, significantly advancing research and clinical applications in neuroscience.[35]
AI-powered cardiac monitoring
Artificial intelligence (AI) enhances the interpretability and diagnostic utility of imaging
modalities such as cardiac magnetic resonance imaging, echocardiography, computed
tomography, and electrocardiography. It strengthens the performance of both screening and
confirmatory tests and generates advanced insights through the integration of high-capacity
computing and analytical frameworks. In doing so, AI enables systems to learn, adapt, and support
clinical decision-making through augmented intelligence. Furthermore, AI facilitates the
automation of several critical medical processes, including diagnosis, risk stratification, and
clinical management, thereby reducing the workload of healthcare professionals and lowering the
likelihood of diagnostic or procedural errors.[36]
AI-powered cardiac monitoring is an emerging technique in preventive cardiology, aligned with
the principles of precision medicine, to continuously assess heart function. “CardioSense” is a
medical AI company focused on combating preventable cardiovascular disease through a platform
that combines multimodal wearable technology with AI.[37] It is recognised as the first wearable to
simultaneously capture three high-fidelity physiological signals that include:
1. ECG: Measures the heart's electrical activity (rhythm).
2. Photoplethysmogram (PPG): Measures changes in blood volume (pulse).
3. Seismocardiogram (SCG): Measures subtle vibrations on the chest wall caused by the
heart's mechanical contraction and blood movement
CardioSense provides comprehensive, non-invasive cardiac assessments from the hospital to the
home, offering visibility into the heart's electrical activity, mechanical function, and
hemodynamics. Basically, “CardioSense” is aiming to move beyond traditional rhythm
monitoring by leveraging advanced sensor fusion (ECG+PPG+SCG) and AI to provide non-
invasive, continuous hemodynamic and mechanical insights for proactive, precision cardiac care,
particularly for conditions like heart failure. Other AI-based approaches are also being developed
to predict and monitor various cardiac conditions.[37]
ECG monitoring - Continuous heart rhythm analysis.
The purpose of this is to detect abnormalities in the heart's rhythm, known as arrhythmias, atrial
fibrillation and ventricular tachycardia. Continuous monitoring is crucial for catching transient or
asymptomatic events.
H-R variability - Detecting stress, emotional states, and cardiac function.
This refers to Heart Rate Variability (HRV), specifically searching for any variation in time
between consecutive R-peaks (the main spike in an ECG complex, representing ventricular
depolarization). The time between two successive R-peaks is the R-R interval.[38] HRV reflects the
net outcome of the effects between the sympathetic (“fight-or-flight”) and parasympathetic (“rest-
and-digest”) autonomic nervous systems, that regulate heart rate. HRV is often associated with
increased stress, anxiety, or emotional strain. It is a known independent predictor of increased
mortality in patients with conditions like heart failure or post-myocardial infarction.
Entropy analysis - Measuring cellular disorder and dysfunction
Entropy analysis, in this context, is a non-linear analysis technique applied to the R-R interval time
series (HRV). Entropy is a measure of randomness or complexity in a signal. A healthy, complex,
and dynamic system exhibits higher entropy. Loss of entropy or complexity in signals leads to
“disorder”, indicating that the system is becoming more rigid, or dysfunctional. It is used to
discriminate between different cardiac states (e.g., normal rhythm vs. life-threatening arrhythmias
like Ventricular Fibrillation) or different disease states (e.g., healthy vs. heart failure).
Early detection - Identifying sudden cardiac death (SCD) risk before symptoms appear.
The ultimate goal of identifying SCD risk, before symptoms appear, is achieved by combining the
above analyses, often with machine learning/AI Research. The complex HRV features (including
non-linear metrics like entropy) analysed from continuous ECG data can be used to build
predictive models. These models aim to quantify an individual's SCD risk index, minutes or hours
before a fatal event, allowing for potential pre-emptive intervention.
In summary, a state-of-the-art cardiac monitoring system that uses advanced signal processing
such as entropy analysis on beat-to-beat timing data (R-R Variability) derived from continuous
ECG readings can help predict critical events before they manifest.
Precision medicine in the context of integrative healthcare
The integration of allopathy, ayurveda and homeopathy is a novel idea for the best holistic method
of treatment.[39] Allopathic medicines are developed on scientific principles and methodology. It
is evidence based to identify the active principle for treatment after many years of laboratory
research work and trial experiments on animals and finally clinical trials on volunteering patients.
It provides quick and targeted symptom relief. Ayurveda focuses on balanced, holistic treatment
emphasizing prevention, lifestyle modifications like diet and long-term healing. Its principles are
inherently centred on individual’s constitution or personalization. Homeopathy offers highly
individualized treatment plans based on the principles of “like cures like” (similia similibus
curentur). It uses ultra diluted active principle for treatment and has a cellular to vascular
approach.[40]
Over the next decade, the development of precision integrative medicine is expected to accelerate,
combining molecular diagnostics with validated traditional medical practices. Policy support will
be essential for the creation of AI-driven platforms that integrate modern biomedical data with
traditional medicine knowledge systems to enable personalized integrative care. Equally important
is the establishment of robust regulatory frameworks to ensure evidence-based, safe, and
standardized integration of traditional and precision medicine approaches. Such a policy-driven
approach can harness traditional medical knowledge while applying rigorous scientific standards
to improve individualized health outcomes. A comprehensive framework is envisaged that
integrates multiple domains: genomic, proteomic, and metabolomic profiling (molecular
precision); rigorous, evidence-based evaluation of complementary therapies (traditional medicine
validation); personalized nutrition, physical activity, and stress-management strategies (lifestyle
genomics); customization of meditation, yoga, and mindfulness practices based on stress-response
genetics and neuroimaging (mind–body precision); and the integration of gut microbiome analysis
with traditional dietary principles to enable personalized nutritional therapy (microbiome
integration).[41] Collectively, this integrative approach respects the wisdom of traditional healing
systems while applying rigorous scientific methodology to optimize individual patient outcomes.
It can also accommodate homoeopathy. Although the molecular mechanisms underlying
homoeopathic systems remain debated, precision medicine tools may be used to explore
individualized treatment responses by analysing enzyme function, neurochemical changes,
immunological reactions, and metabolic alterations with the aim of investigating potential
regulatory effects of ultra-dilute preparations.[40]
Omega fatty acids and cellular health
Omega fatty acids are considered crucial for cellular health. Omega-3 polyunsaturated fatty acids
(PUFAs), particularly docosahexaenoic acid (DHA), can increase membrane fluidity by
preventing lipid bilayer from packing tightly due to the presence of the kinks produced by double
bonds. Proper membrane fluidity is vital as it affects the rotation and diffusion of proteins and
other biomolecules within the membrane, which in turn affects cell function. Cells can employ
compensatory mechanisms, like increasing saturated lipids, to counteract the fluidizing effect of
incorporated PUFAs and maintain membrane biophysical properties. Omega-3 fatty acids,
especially eicosapentaenoic acid (EPA) and DHA, support heart health by reducing inflammation
in blood vessels, helping to lower triglyceride levels, and potentially improving blood pressure.
Some studies suggest that they may reduce the risk of cardiovascular disease (CVD) and death
from CVD, and help prevent blood clots.[42] However, evidence on preventing heart attacks or
strokes specifically from supplements is mixed. Omega-3 PUFAs (EPA and DHA) are structural
components of neuronal membranes, influencing their function through membrane properties and
also act as precursors for signalling molecules. They are linked to an improved state of mental
health, potentially by modulating the gut-brain axis, reducing inflammation, and regulating the
stress response (HPA axis). Some research indicates that omega-3 long-chain polyunsaturated
fatty acids (LC-PUFAs) may improve sleep quality, specifically showing a significant
improvement in sleep efficiency in some trials. Omega-3s contribute to overall cellular integrity
and evidence suggests that they may lower the risk of certain cancers, such as colorectal cancer.[43]
Their structural role in membrane further supports this protective effect.
The future: AI-enhanced biohacking
AI-enhanced biohacking is the integration of AI with personal health optimization practices to
achieve unprecedented levels of self-improvement. It involves feeding complex, high-volume
personal data sourced from wearables, genomics, and biomarker testing into machine learning
algorithms.[44] This powerful analytical capability allows biohackers to move past generalized
health advice and generate hyper-personalized, adaptive strategies for fitness, nutrition, and
cognitive function, thereby rapidly accelerating the path towards peak performance and enhanced
longevity. The combination of ayurvedic principles with AI technology represents an exciting
frontier in personalized medicine, where ancient knowledge meets cutting-edge technology to
optimize human health at the cellular level. This holistic approach to healthcare - linking genetics,
AI, and precision medicine under the theme "Don't Miss a Beat" - truly represents the future of
medicine where technology serves human wellbeing while honouring traditional healing
wisdom.[45]
Telemedicine
Distance healthcare, includes services provided through audio and video technology, extending
healthcare services to remote areas, especially during pandemics, and improving overall healthcare
access. Telemedicine enables healthcare providers to offer services, consultations and patient
monitoring without being physically present, thereby increasing accessibility.[46]
Robots
Robots have the potential to completely transform the medical field (Figure 3). The growing
integration of robotics in medicine is driven by advancements in computing power and
miniaturization. AI-medical robots are increasingly recognized for their employment in surgery,
particularly for the precise manipulation of surgical instruments through small incisions, guided
by computers and software. These systems provide a precise, controlled surgical field, visualized
in three dimensions through high-definition, magnified imaging. The main advantages of robot‐
assisted surgery for patients include fewer incisions, less blood loss, and quicker recovery, similar
to the benefits of laparoscopic surgery. Robotics also hold promise for replacing traditional
endoscopy. Small robots can be directed to precise areas to perform tasks such as obtaining a
biopsy or cauterizing a bleeding vessel. Microrobots could be used to enter blood vessels to deliver
medication or radiation therapy to a targeted area. Additionally, robotic endoscopic capsules that
can be swallowed may patrol the digestive system, collect data and transmit diagnostic information
back to the operator.[47]
Figure 3: Robot used in Hridayalaya Cardiology and Robotic Research Centre
Multi‐omics
A wide array of molecular research fields with the suffix “-omics”—including genomics,
epigenomics, proteomics, transcriptomics, metabolomics, and microbiomics—play pivotal roles
in advancing precision medicine. Among these, metabolomics and genomics deserve special
mention.[36]
Metabolomics involves the study of specific metabolites for diagnostic purposes. For instance, a
metabolic transition occurs in the failing myocardium. Hence, metabolic profiles of patients with
systolic heart failure can be assessed by examining serum and breath samples for clinical purposes
such as diagnosis and prognosis. Extensive databases of metabolites are now available, many of
which are implicated in heart failure. For example, metabolite clusters such as factor 4 (branched-
chain amino acid metabolites) and factor 9 (urea-cycle metabolites) can aid in the diagnosis of
coronary artery disease (CAD).[48] Elevated levels of branched-chain amino acids (BCAAs)—
including leucine, isoleucine, and valine—themselves have been implicated in CAD, often
reflecting metabolic impairments such as insulin resistance and abnormal protein metabolism.
Acylcarnitines, which are byproducts of mitochondrial fatty acid oxidation, serve as markers of
dysregulated mitochondrial metabolism and are associated with CAD, heart failure, and insulin
resistance.[49] Cholesterol and lipids (such as triglycerides and phospholipids), fatty acids, glucose,
and ketone-related metabolites are also considered important metabolic risk factors.[49] Pharmaco-
omics is another important discipline that contributes to the development of drugs tailored to the
needs of particular subpopulations; such drugs are not prescribed to non-responders, thereby
preventing unwanted adverse effects and making treatment more cost-effective.[36]
Advances in genomics have also contributed significantly to the diagnosis and treatment of genetic
diseases, including myocardial infarction (MI). DNA sequencing and gene identification have
enabled the development of new therapeutic agents. For instance, the identification of genes such
as CNR2, DPP4, GLP1R, SLC5A1, HTR2C, and MCHR1 may facilitate the development of drugs
for type 2 diabetes or obesity without increasing cardiovascular risk. Similarly, dual antiplatelet
therapy (DAPT) is routinely used to reduce the risk of thrombosis or MI. However, patients with
loss-of-function CYP2C19 mutations have a higher likelihood of ischemic events when treated
with standard DAPT. In such cases, a genotype-guided P2Y12 inhibitor, such as ticagrelor, is
recommended as an alternative.[50]
The term “multi‐omics” refers to a research approach that combines several “omics” data sets from
various fields of study. This integrative analysis is crucial because biological functions are not
governed by any single type of molecule in isolation; instead, they result from complex, dynamic
interactions across all these molecular levels. By combining these diverse datasets, researchers
gain a far more holistic understanding of cellular processes, the progression of diseases, and the
overall health state of the patient.[51] The ultimate goal is to bridge the gap between a patient’s
genetic code and the observable traits, or phenotype. This comprehensive molecular profiling is
foundational to advancing both fundamental biological research and the implementation of
precision medicine.
Patient selection, implementation, and risk stratification protocols in precision medicine
The identification of candidates for precision medicine interventions follows a multi-tiered
screening approach that balances clinical efficacy with economic feasibility. However,
establishing common criteria for patient selection remains challenging due to the inherent
complexity involved. Current protocols incorporate family history assessment alongside validated
genetic risk tools, such as polygenic risk scores for cardiovascular disease and cancer
predisposition. Population-based screening programmes prioritise high-risk groups; for example,
BRCA1/2 testing is recommended for individuals with a strong family history of breast or ovarian
cancer. Furthermore, algorithmic risk prediction models further refine selection by integrating
electronic health records, lifestyle variables, and preliminary biomarker data.[4] Presymptomatic
disease detection is facilitated by routine health surveillance and emerging liquid biopsy
technologies. [27-29]
Healthcare costs rise with both age and disease burden. A major driver of escalating expenditure
is the growing prevalence of chronic and multiple long-term conditions, largely linked to lifestyle
changes and population ageing. Globally, an estimated 33% of adults live with multiple long-term
diseases, a figure that rises to nearly 75% in developed countries.[52] Age is a critical determinant
of cost: in the United States, annual healthcare expenditure for individuals over 65 years is
approximately 2.5–5 times higher than that for younger age groups, depending on comorbidity
profiles. Escalating pharmaceutical costs contributes to healthcare inflation, with the US drug
budget projected to grow at an average annual rate of 6.1% between 2020 and 2027. The adoption
of new and often expensive medical technologies also adds to overall costs.[52]
Conversely, improved access to large-scale, high-quality multi-omics patient data, advances in
data analytics, and systematic drug repurposing have ushered in an era of more affordable and
precise personalised medicine. These approaches have the potential to reduce overall healthcare
expenditure by identifying the most effective therapies for individual patients, improving
outcomes while minimising unnecessary treatments.[51] In the context of rising drug prices and
increasing pressure on healthcare budgets, such strategies may prove critical to the future
sustainability of healthcare systems.[52] In short, the economic sustainability of precision medicine
depends on judicious implementation. Initial screening using cost-effective targeted genomic
panels has become more affordable, with costs reduced by nearly 50% in several centres in India.
Cost reduction could be achieved by prioritising conditions in which precision interventions offer
clear cost benefits, such as avoiding ineffective chemotherapies and preventing adverse drug
reactions. Increasingly, precision diagnostics are covered by insurance when these tests directly
guide clinical decisions, as in the case of Oncotype DX in breast cancer and pharmacogenomic
testing in psychiatric treatment. It is a relief to patients that the cost of whole-genome sequencing
has declined substantially (by approximately 50%) in recent years, facilitating the wider
application of precision medicine. Population-level screening programmes for actionable genetic
conditions, such as familial hypercholesterolaemia, are also found to be cost-effective, through
early intervention. Likewise, Machine learning algorithms reduce interpretation costs and enable
automation of routine analyses.[36,44]
Future of healthcare
The future of healthcare is individualized and driven by advancements in nanotechnology, IT, and
genetics. Advances in anaesthesia and minimally invasive surgical practices are expected to
decrease inpatient volumes while increasing outpatient examinations and treatments. Advances in
oncogenomics and cancer pharmacogenomics, driven by next‐generation sequencing technology,
are anticipated to personalize cancer screening and diagnosis.[53] AI systems will shift healthcare
from traditional models to cost‐effective, data‐driven disease management strategies, furthering
immunomics and drug discovery for better preventive strategies. Wearable healthcare and Internet
of Medical Therapy (IoMT) devices equipped with biosensors and transducers promise continuous
health monitoring and reduced hospital visits. [27-29] Collectively, such wearables can support
telemedicine and telehealth by enabling continuous monitoring while serving as platforms for
secure health data generation and storage.
The surge of innovations, encompassing AI, telemedicine, precision medicine, and big data
analytics (BDAs), has revolutionized the healthcare sector.[54] The transformative potential of these
advancements becomes evident as we gaze into the future of healthcare. The healthcare sector holds
tremendous promise, driven by the convergence of state‐of‐the‐art technologies, data‐driven
methodologies, and a dedicated commitment to mitigating healthcare disparities. This synergy
between healthcare and technology presents exciting possibilities, paving the way for personalized
treatments that account for individual variations and enhanced patient outcomes. Moreover, this
technological evolution has the potential to establish a more accessible healthcare system,
dismantling barriers and delivering quality medical services to diverse populations, including those
residing in underserved areas.[54]
Conclusion
As we move into the next decade, collaborative endeavours between healthcare and technology
are poised to redefine our approach to wellness. The journey towards a promising future requires
active engagement, innovation, and careful navigation of ethical considerations within the
healthcare sector. By embracing these principles, stakeholders in the healthcare ecosystem can
collectively contribute to a landscape that is not only technologically advanced and efficient but
also profoundly centred on the well‐being of individuals and communities. This transformative
trajectory holds the potential to create a healthcare environment that is responsive to the evolving
needs of society and dedicated to fostering optimal health outcomes for all.
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