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A digital revolution is literally sweeping through the pharmaceutical and healthcare industry in the world, and India is no exception. Powered by artificial intelligence (AI), this revolution is reshaping the entire healthcare landscape.
The Indian healthcare space, particularly after the Covid pandemic, is fast evolving with the adoption of technology like AI and machine learning (ML). It is bringing in new paradigm shift in healthcare practices in the country and in fact it is revolutionising the field of medicine by improving the accuracy of diagnoses and providing new methods of treatment.
The Indian healthcare sector increasingly views AI as a strategic imperative, enabling next-generation treatments and driving systemic efficiency across the sector.
According to Arvind Vaishnav, head, Philips Innovation Campus (PIC), AI holds the promise of a transformative leap in healthcare, enabling clinicians to meet rising medical challenges with greater speed, accuracy, and confidence. In particular, within medical imaging and diagnostics, AI is streamlining processes, enhancing accuracy, and helping expand access to quality care.
From our perspective, these developments are not just about technological progress but about democratizing expertise and making healthcare more equitable. We firmly believe AI should be embedded in care workflows to help clinicians enhance productivity and support improved outcomes, contributing to a more human-centred healthcare system, he added.
AI application in diagnosis
One of the most promising applications of AI in medicine is in the field of diagnosis. Medical diagnosis with AI brings in time-bound accuracy for speedy decisions to treatment access and is bringing in major transformation in the way a primary physician will extend treatment protocols. By analysing large amounts of medical data and identifying patterns that may not be apparent to the human eye, AI can assist doctors in identifying diseases early when they are most treatable.
AI algorithms can be trained to recognize signs of cancer in medical images such as mammograms or X-rays. This can help to improve the accuracy of diagnoses and reduce the number of false negatives. AI can also be used to analyse patient records and identify patterns that may indicate a particular disease. Further AI algorithms can be used to create personalized treatment plans for patients based on their medical history and current condition. They can also be deployed in clinical decision support systems where optimum treatment strategies can be developed by analysing vast amounts of patient-specific data such as clinical features, laboratory, and radiological investigations.
In widely used imaging techniques such as ultrasound, AI is making care more consistent and scalable. Traditionally, ultrasound has been heavily dependent on the skill of the operator, leading to variability across users. Now, AI runs behind-the-scenes while a clinician is capturing images. It supports clinicians by automatically identifying and suggesting effective images for certain applications like for instance strain or ejection fraction, as required. This may help reduce variability, improve productivity, and support more consistent deployment of Ultrasound across diverse clinical settings, Arvind Vaishnav said.
Computed tomography (CT) imaging too is undergoing a similar transformation.
AI-based reconstruction is designed to deliver sharper images at lower radiation doses, which can enhance CT’s role in screening, diagnosis, and interventions, subject to clinical evaluation.
Diagnostic tools like MRIs and CT scans have helped doctors diagnose illnesses with more accuracy. In future, while AI and ML will be used for patient improvement, data informatics will provide a better understanding of the disease.
It can provide a mechanism for patients to provide their clinicians with critical information to ensure the scope for better care. The big development on the healthcare landscape is the use of wearable devices which will gather the data of health and transmit it in real-time for faster treatment access. Wearables are designed for chronic condition monitoring.
Beyond diagnostics, AI is also beginning to transform how clinicians perform complex interventions. In interventional cardiology, for instance, real-time imaging fused with AI-driven guidance is supporting more precise navigation inside the human body, with the potential to enhance procedural safety.
These advanced systems can serve as digital co-pilots, assisting cardiologists in steering catheters with improved accuracy, with the potential to enhance safety, speed, and consistency of procedures. As these technologies mature, similar applications are being explored in fields such as oncology, neurology, and minimally invasive surgery, where AI may contribute to making procedures safer, faster, and more broadly accessible, said Vaishnav.
AI use in drug development
Another notable application of AI is in drug development. Here AI algorithms can be used to analyse vast amounts of data from clinical trials and identify new drug targets. It can also be used to control robots performing complex surgeries, such as brain surgery, with greater precision and accuracy than human surgeons. AI will fundamentally transform medicine in its diagnostics and patient care as technology engages the doctor in two ways.
One is the supportive care extended to patient and the other is to reduce the doctor’s human cognitive load. From a patient perspective, AI gives attention to genomics and epigenetics. The former is the study of information encoded within the full DNA sequence.
The latter focuses on how DNA is organised and regulated in the cell. Genomics and epigenetics bring in robustness to the structured make-up of the human body. It indicates the time dependent wear and tear durability. Therefore, genomic + Big Data provides patterns of the body condition with AI. These are new emerging areas which are seen to be dominant mechanisms for understanding how the body system functions.
One of the most profound changes is in drug discovery. Developing a new drug is tediously slow, expensive, and failure-prone. In India, this has been further complicated by limited infrastructure and high development costs. AI is now tackling these problems head-on.
By analysing vast chemical libraries, simulating molecular interactions, and identifying promising candidates early in the process, AI algorithms are saving development timelines and substantially improving success rates. What once took a decade can now be done in months. This AI-driven transformation is allowing Indian companies to move beyond their traditional role of manufacturing generics into the high-stakes world of original drug development. It is not just about cost-effectiveness anymore; it is about discovery and innovation.
Besides all these, the adoption of technology will also allow surveillance. These cover molecular markers where with AI and ML will see the physician understanding the disease. Besides, digital phenotyping helps precision mapping which leads to better diagnosis. All these as the paradigm shift in medicine as it will enhance patient empowered empathy, not progression of ageing and disease spread. All these transformations with technology will lead to high value patient engagement and give the doctors time for themselves as diagnostics will be faster providing clarity on the disease progression.
Medical schools’ role in AI deployment
All said and done, medical schools are playing a significant role in the deployment of AI in healthcare by incorporating AI courses into their curriculum and promoting research in this field. This is helping to train a new generation of healthcare professionals who are familiar with AI and its potential applications in healthcare. Medical schools are also establishing partnerships with engineering colleges and start-ups to develop and test new AI-powered tools and systems.
These partnerships provide students with practical experience working with AI technologies and can lead to the creation of new AI-powered solutions that can be used in clinical settings. By implementing AI technologies in clinical settings, medical schools are helping to accelerate the deployment of AI in healthcare.
Ethical considerations
While AI has the potential to revolutionize the field of medicine and improve patient outcomes, there are also potential concerns of bias, data privacy and security, clinical validity, interoperability, and ethical considerations. The regulation and standardization of AI in medicine are still in their early stages, and there is a risk that the technology may be adopted and used before proper oversight is in place. AI algorithms must be transparent and explainable so that healthcare professionals can understand how decisions are being made and ensure that they are fair and accurate.
There can be no difference of opinions about the fact that Indian pharma is witnessing a transformative surge as AI reshapes every stage of drug development, manufacturing, and distribution. With advanced algorithms enabling faster data analysis, predictive modelling, and automation, AI is significantly reducing research timelines, improving accuracy, and enhancing decision-making across the sector.
No doubt, AI is now all set to revolutionize the pharmaceutical industry through breakthroughs in drug discovery, personalized medicine, and healthcare solutions.
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