Two 21st-century forces for change—Biotechnology and Artificial Intelligence (AI)—are redefining the future of medicine. Their integration is driving unprecedented advances in genomics, diagnostics, drug discovery, and personalized medicine with the potential for more targeted, affordable, and customized care. What science fiction a few years ago is now going to become a reality in the near future: artificial intelligence-based algorithms are now able to decode genetic code, forecast disease, and even help in personalizing treatment based on someone’s very specific biological map. The report discusses the technology landscape, global applications, and economic considerations of AI-based biotechnology in developing individualized treatment, i.e., Indian and international. The Genomics Revolution and the AI Role
Genomics is the basis of today’s precision medicine. The human genome has more than 3 billion pairs of DNA base, and de-encoding this dense information is required to predict, prevent, and cure disease. The human genome was a decade-long, billion-dollar project but can be sequenced in less than $500 in hours with next-generation sequencing (NGS). The actual challenge then is what to do with and how to manage this mountain of information.
In this, AI has played the role of a game changer. Deep learning models can read genomic information to identify genetic mutations, variants, and disease-related biomarkers like cancer, diabetes, and low-penetrance genetic disorders. AI models, for instance, can read polygenic risk scores (PRS) to estimate the individual’s risk to diseases like coronary artery disease or Alzheimer’s.
One of the most significant contributions of the decade is AlphaFold, from Google’s DeepMind, that was predicting 3D protein structure at almost experimental quality using AI. AlphaFold had constructed over 200 million protein structures through 2023, representing all except a negligible minority of proteins identified to date, significantly accelerating biological research and even the design of drugs.
AI in Personalized and Precision Medicine
Personalized medicine is the application of medical treatment customized to the unique characteristics of each patient. It is the application of genomics, proteomics, lifestyle data, and environmental data to guide patient care. It is facilitated by AI that combines and processes large amounts of heterogeneous data to provide predictive models and treatment regimens.
For example, in oncology, AI software analyzes the tumor genetic profile to determine the best chemotherapy or immunotherapy. AI predicts drug metabolism and toxicity from one person to another using genetic variation. IBM Watson for Genomics, for instance, scans patients’ genes and cross-matches them with relevant clinical trials or treatments. Clinical trials have shown that AI decisions can potentially improve treatment in some cancers by up to 30%.
The precision medicine market was USD 73.15 billion in 2022 and will be USD 175.64 billion in the year 2029, with a CAGR of 13.6%, as per Fortune Business Insights. The role of AI in growth is increasingly important.
AI in Drug Discovery and Development
The conventional drug discovery process costs time and money—typically more than 10 years and $2.6 billion to introduce a new medicine to the market. AI is significantly reducing the timeline by facilitating:
Virtual screening of potential medicines
Predictive modeling of drug effectiveness and toxicity
Optimization of clinical trial design
Others, such as Insilico Medicine, Exscientia, and Atomwise, have led the way in using AI platforms to identify drug candidates in a matter of months as opposed to years. Exscientia, for example, designed an anti-cancer drug in less than a period of 12 months and, in 2021, reached clinical trials.
In India, artificial intelligence-powered biotech start-ups like Qure.ai, Predible Health, and AIndra Systems are making enormous progress in diagnostics, imaging, and predicting drug response. These start-ups with the help of BIRAC and NASSCOM’s CoE for AI are facilitating innovation at the interface of life sciences and AI.
Transforming Diagnostics and Prognostics
AI is also making a significant contribution to diagnostics—particularly to the interpretation of medical images, histopathology slides, and genetic sequences. AI diagnostic computers are utilized to diagnose disease early in order to enhance survival and prognosis.
For instance, radiology algorithms that employ AI can identify breast cancer at 90%+, in many cases identifying abnormalities that cannot be seen by the naked eye. In diagnostic genomics, AI facilitates the analysis of sequencing data to identify inherited disease, carrier states, and recessive gene mutations.
In rural India, for example, AI diagnostics plug gaps in healthcare by offering wider access to intelligence. With AI platforms such as the AI100 platform introduced by SigTuple, blood smear analysis is done automatically so that pathologists can reach conclusions faster and more accurately.
Biomarker Discovery and Therapeutic Targeting
Identification of appropriate biomarkers is relevant to the goal of target treatment identification. Detection of correlations is achievable by AI from high-dimensional data. One such point is that deep learning can detect drug sensitivity or onset signaling of disease patterns of gene expression.
Machine learning models trained using vast databases like The Cancer Genome Atlas (TCGA) can identify novel drug targets or repurpose drugs for novel indications, lowering R&D expense as well as the efficacy of treatment.
Challenges and Ethical Concerns
As promising as it is, AI-biotech interface has issues to boot:
Data Privacy: Extremely personal genetic information needs to be handled with high-encryption as well as ethical data stewardship.
Bias and Fairness: Biased information on which AI programs are trained will more than likely produce biased outputs.
Interpretability: Doctors at times demand AI machines to reveal information, thus recommendations can be verified.
Regulatory principles too need to keep up with innovation. The US FDA, EMA, and Indian CDSCO are inclining toward new frontiers in approvals of AI-based treatment and diagnostics but there is harmonization still going on.
India’s Role and Opportunities
India. It has all the potential to become a world leader in AI-powered biotech innovation in the perfect blend of huge clinical data, decently good startup ecosystem, and government support. With more than 5,000 biotech and more than 500 health-tech startups leveraging AI, India has a fantastic platform to propel precision medicine.
Government initiatives such as National Digital Health Mission (NDHM), AI for All, and Genomics India Project are creating a data-centric, targeted care ecosystem. The biotech industry of India will rise from USD 80 billion in 2023 to USD 150 billion in 2025, and AI will be one of the growth drivers as a perfect diagnostic, drug discovery, and patient management tool.
Conclusion
AI-based biotech advancements are not just revolutionizing the science of biology—redefining the practice of medicine. Writing information into biology, AI facilitates a shift from reactive to anticipatory, one-size-fits-all to individualized care. From solving the code of the genome to being able to predict response to therapy, AI is bringing precision medicine into being.
With continued investment, regulatory innovation, and international partnership, the marriage of biotechnology and AI will transform medicine as it currently exists—to actualize the promise of more personalized, preventive, and patient-driven care for everyone.
Prepared by
Dr. Anam Giridhar Babu,
Associate Professor, Department of Basic Sciences, SR University, Warangal 506371, Telangana, India.
