Imagine a future where artificial intelligence helps researchers understand a disease that has remained a mystery for decades.
It identifies a previously overlooked biological mechanism.
It helps scientists discover a promising molecule.
Researchers develop a treatment candidate in considerably less time than traditional approaches might have required.
That sounds like an extraordinary achievement.
And it would be.
But I want to ask another question.
What happens if the scientific AI drug discovery moves faster, but the patient still has to wait years for the treatment?
This is something I have been thinking about as artificial intelligence becomes increasingly important in scientific research.
On October 12, 2026, Google published an update describing how its AI technologies are being applied to disease research, including genomics, cellular biology, and the search for better treatments.
The same day, India’s Department of Science and Technology announced ₹86 crore in financial support for Peptris Technologies, a Bengaluru-based company developing AI-discovered drug candidates.
These are encouraging developments.
They suggest that artificial intelligence is moving beyond generating text, writing software, and answering questions.
It is becoming part of the scientific process itself.
I consider this one of the most exciting directions in AI.
But I also believe we need to change how we evaluate its success.
We Are Entering an Extraordinary Period of Scientific Possibility
For most people, the AI revolution is associated with chatbots.
They use AI to draft emails, summarize documents, create presentations, write code, or generate images.
These applications have created enormous public interest.
However, I suspect that some of AI’s most important long-term contributions may happen in places the general public rarely sees.
Research laboratories.
Universities.
Biotechnology companies.
Hospitals.
Scientific institutions.
And organizations working to understand how complex biological systems function.
Consider the human genome.
It contains approximately three billion DNA base pairs.
Scientists have made remarkable progress in understanding genetics, but many aspects of how genetic variations affect biological processes remain difficult to interpret.
Google DeepMind’s AlphaGenome Atlas is designed to help researchers investigate the potential effects of single-letter changes in human DNA.
The system provides predictions that scientists can use when studying molecular biology and possible disease mechanisms.
That is remarkable.
But it is important to understand what this technology does not automatically provide.
A prediction about a genetic variant is not a medical diagnosis.
And a better understanding of disease biology is not the same as an approved treatment.
Those distinctions may sound obvious, but they matter.
Because as AI capabilities advance, I think the temptation to confuse a scientific breakthrough with an immediate healthcare breakthrough will become stronger.
Finding a Promising Molecule Is Not the Same as Developing a Medicine
Let’s consider drug development.
A researcher might use AI to identify a promising biological target.
Another AI system could help generate possible molecular structures.
Computational tools might evaluate how those molecules interact with particular proteins.
The results could help researchers decide which candidates deserve further investigation.
This can be valuable.
It may reduce unnecessary experimentation and help scientists explore possibilities they might otherwise miss.
But the process does not end there.
A promising molecule must still undergo rigorous experimental testing.
Researchers need to examine whether it works as expected, whether it is toxic, how it behaves inside living systems, and whether it can be manufactured consistently.
Human clinical trials, where appropriate, must establish safety and effectiveness.
Regulatory authorities must evaluate the evidence.
And even after a medicine is approved, healthcare systems must make it available to patients.
This is where I see a critical challenge.
AI may accelerate discovery without accelerating every other part of the journey from the laboratory to the patient.
In fact, a major August 2026 perspective published in Nature Reviews Drug Discovery highlighted the limited evidence so far that AI advances have substantially improved what matters most: delivering safer and more effective medicines to patients faster.
I appreciate this kind of research because it encourages us to look beyond the excitement.
It asks whether the technology is creating measurable real-world improvements.
And I believe that is exactly the question the AI industry needs to become more comfortable answering.
My Experience With Process Excellence Has Influenced How I See This
Through my work in process excellence, technology, and entrepreneurship, I have learned something important.
Improving the performance of one activity does not necessarily improve the outcome of the entire system.
Imagine a company that increases its manufacturing capacity by 300%.
That sounds impressive.
But what if its packaging department cannot handle the additional production?
Or its distribution network remains unchanged?
The company may simply move its bottleneck from one department to another.
Now consider the same principle in scientific research.
If AI makes it significantly easier to identify potential drug candidates, but experimental validation remains slow, what happens?
The next bottleneck becomes more visible.
If clinical trials are difficult to organize, expensive to conduct, or slow to recruit participants, discovering more candidates may not automatically produce faster patient outcomes.
If manufacturing capacity is limited, approved treatments may still face supply constraints.
And if the eventual medicine is unaffordable, the scientific breakthrough may have little practical meaning for people who cannot access it.
This is why I think AI in healthcare needs to be evaluated as part of a much larger system.
Not just as a model.
Not just as a laboratory tool.
But as one component of a process that ultimately exists to improve human health.
India Has an Opportunity Worth Taking Seriously
The latest developments in India particularly interest me.
On October 12, the government announced financial support of ₹86 crore for Peptris Technologies under the Research, Development and Innovation Fund.
The company’s VIGOR programme has a total project cost of ₹172 crore.
It aims to advance four AI-discovered drug candidates through preclinical development and toward readiness for clinical evaluation.
I find the emphasis on moving from discovery to development particularly important.
This is the stage where many promising scientific ideas face difficult questions.
Can the underlying hypothesis be validated?
Can a candidate demonstrate meaningful biological activity?
Can the necessary studies be conducted to appropriate scientific standards?
Can the technology move beyond a promising laboratory result?
The government-supported programme does not guarantee that these candidates will become approved medicines.
Many drug candidates fail during development, even after encouraging early results.
But it represents an attempt to support the expensive and demanding process of translating scientific ideas into potential treatments.
I believe India needs more efforts of this kind.
For decades, India has developed significant strengths in pharmaceuticals, generic medicines, medical professionals, and scientific talent.
Artificial intelligence creates an opportunity to build on those strengths.
We should not limit our ambition to becoming users of AI tools developed elsewhere.
We should aim to develop original scientific capabilities, research platforms, intellectual property, and globally competitive healthcare innovations.
I believe the real opportunity is not simply AI adoption.
It is AI-led scientific innovation supported by the institutions needed to turn research into products and services.
We Should Be Careful About the Idea of AI-Discovered Cures
There is another concern I want to raise. However, I discussed this topic thoroughly in my last article, AI Deepfakes: When Everything Can be Faked, What to Trust; still, I want to highlight this here.
As artificial intelligence becomes more involved in scientific research, we will probably see increasing numbers of headlines describing AI-designed drugs or AI-discovered treatments.
These developments may be scientifically important.
But the language we use matters.
A molecule identified by AI is not necessarily a cure.
A successful laboratory experiment is not necessarily evidence that a treatment will work in humans.
And promising early clinical results do not guarantee long-term effectiveness or safety.
Healthcare involves human lives.
The consequences of being wrong can be serious.
I believe AI researchers, technology companies, investors, and the media have a responsibility to communicate the distinction between discovery, experimental validation, clinical evidence, and medical approval.
Excitement is justified.
Exaggeration is not.
In fact, I think responsible communication will become part of building public trust in AI-assisted medicine.
When a research team makes an important discovery, we should celebrate it for what it is.
We should not turn it into a promise that science has not yet established.
The Real Opportunity Is Connecting AI With the Entire Healthcare Ecosystem
One development I am watching is the emergence of more integrated AI-driven research processes.
A review published in npj Drug Discovery in October 2026 discussed the potential of connecting AI-based molecular design, multimodal analysis, and automated experimentation.
The idea is that computational predictions and experimental evidence can inform one another more effectively.
I find that direction especially interesting.
But I would like the thinking to extend even further.
Imagine an environment where researchers, hospitals, biotechnology firms, manufacturers, and healthcare providers can work more effectively with one another.
Scientific data is documented consistently.
Researchers can understand which experiments have already been conducted.
Clinical teams can identify appropriate study populations while protecting patient rights and privacy.
Manufacturing requirements are considered early enough to influence development decisions.
Healthcare providers have access to reliable evidence about which treatments work and for whom.
Artificial intelligence might help coordinate parts of this process.
But creating such a system would require far more than installing an AI model.
It would require shared standards, responsible data governance, institutional cooperation, funding, and sustained scientific leadership.
This is an area where I believe systems thinking becomes particularly valuable.
The most sophisticated AI model may not create the greatest healthcare improvement if it operates inside an ineffective system.
There Is Also a Question of Who Benefits
Suppose AI eventually helps reduce the cost and time required to discover certain medicines.
That would be an extraordinary achievement.
But would patients experience lower treatment costs?
Would rare diseases attract more research investment?
Would rural hospitals benefit?
Would developing countries gain access to new therapies sooner?
Or would many of the gains remain concentrated among organizations with the financial resources to develop and commercialize them?
I don’t think these questions should be postponed until the technology becomes mature.
They should influence how AI healthcare ecosystems are designed today.
For India, this means thinking beyond research laboratories.
We need to consider the quality and representativeness of health data, the availability of clinical research infrastructure, scientific training, affordable healthcare delivery, and the protection of sensitive patient information.
We also need to recognize that models trained on particular populations may not always generalize reliably to others.
A technology that performs impressively in one research setting may require additional validation before being trusted in a different clinical environment.
Healthcare innovation should not become another area where technical capability advances faster than our ability to distribute its benefits.
What I Would Like to See India Do Differently
If I were contributing to a national strategy for AI-driven healthcare innovation, I would focus on three broad priorities.
First, I would encourage stronger collaboration between AI researchers, medical institutions, pharmaceutical companies, and universities.
Breakthroughs often happen when people from different disciplines begin working on the same problem.
Second, I would place greater emphasis on experimental validation and clinical translation.
India needs not only researchers capable of building models, but also laboratories, testing infrastructure, clinical research capabilities, and institutions capable of evaluating whether those models produce useful scientific results.
Third, I would encourage funding models that recognize the long timelines and uncertainty involved in healthcare innovation.
A biotechnology company cannot necessarily be evaluated using the same expectations as a software startup launching a consumer application.
Drug development involves years of research, substantial capital, and a real possibility of failure.
Investors and policymakers need to understand those differences.
My broader suggestion would be to connect scientific ambition with practical institutional capacity.
Building an AI model is an achievement. Building an ecosystem capable of turning scientific advances into better healthcare is a much larger one.
I Believe We Need a Different Measure of AI Success
Much of the AI industry measures progress through technical benchmarks.
Accuracy.
Reasoning capability.
Computational efficiency.
Model size.
Performance against competing systems.
These measurements are useful.
But when we discuss AI in healthcare, I believe we should eventually place more emphasis on different outcomes.
Did the technology help researchers identify a valid biological mechanism?
Did it improve the quality of scientific decisions?
Did it help reduce unnecessary experiments?
Did it contribute to safe and effective treatments?
Did patients benefit?
Were those benefits accessible beyond a small group of people?
We will not be able to answer every question immediately.
Scientific progress takes time.
But the questions we choose to ask influence what we choose to build.
And I think the AI industry needs to become more focused on outcomes that matter beyond the technology itself.
Final Thoughts: The Next Great AI Breakthrough May Not Look Like a Chatbot
I am genuinely optimistic about artificial intelligence in science.
I believe there is enormous potential in combining advanced computational models with biotechnology, medicine, genetics, and experimental research.
The possibilities are exciting.
We may eventually understand diseases that have puzzled researchers for decades.
We may discover new therapeutic approaches.
We may identify promising treatments more efficiently.
And we may develop scientific tools that allow smaller research teams to investigate questions that previously required considerably greater resources.
But I also recognize that none of these outcomes is guaranteed.
Scientific evidence, clinical validation, human expertise, ethical safeguards, and access to healthcare will remain essential.
As a researcher and entrepreneur, I find this one of the most meaningful areas of artificial intelligence because its potential benefits are deeply human.
It is not merely about making a task faster.
It is about understanding life, reducing suffering, and creating possibilities that did not exist before.
Yet I would like us to remember one simple distinction.
The discovery that changes a laboratory is not necessarily the discovery that changes a patient’s life.
The real achievement comes when scientific knowledge becomes something that can safely and meaningfully help people.
That is the AI future I want to see.
Not simply machines capable of understanding increasingly complex biological systems.
But scientific institutions, healthcare systems, and businesses capable of translating that intelligence into benefits for society.
Because ultimately, the success of AI in medicine should not be measured only by how much faster we discover potential treatments.
It should be measured by how much better we become at helping the people who need them.
— Jaideep Parashar
Founder & Director, ReThynk AI Innovation and Research Pvt. Ltd.
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