Amit Phadnis is its CIO and CTO RapidAIworld leader in clinical artificial intelligence.
The role of AI in healthcare is often seen as purely technical, but the reality is deeply human. At its core, AI in healthcare is not about displacing human providers, but helping them flourish.
Over the years, I’ve watched AI move from concept to practice, and what stands out to me the most is how it can give clinicians new ways to understand patient needs. It creates a much-needed partnership between the technology that supports an organization and the people who rely on it every day, both patients and providers.
But what does artificial intelligence look like in practice? And more importantly, why are we looking to AI to improve patient outcomes and care?
Bridging data gaps from older equipment
Today’s clinicians are tasked with connecting data from medical scans, patient medical history, genetics and more. This is often under tight deadlines, alongside the clinician’s many responsibilities to other patients. With the sudden rise of artificial intelligence models that can process massive data sets quickly, it seems a no-brainer to use them in healthcare. But in my experience, leaders need to think of AI in healthcare processes as a challenging yet rewarding task. Data accuracy, privacy concerns and training models are obstacles.
But of these, the most overlooked challenge in building AI for the healthcare industry that I often see is data collection. Organizations need both high-quality data and diverse data sets. Both of these can be challenging. Older CT and MRI machines, for example, may not have the resolution of modern devices. They may produce datasets that are less standardized and more difficult for artificial intelligence systems to process. And tighter health care budgets don’t easily allow for new equipment.
To overcome these limitations, I’ve found that if your AI only learns from a limited set of patient cases, it risks being ineffective—or even harmful—in broader applications. This makes data diversity and quality a top priority in AI development for healthcare.
You need data that reflects different demographics, genetic backgrounds, and clinical situations. This isn’t just a technical requirement, it’s a commitment to make AI work for everyone. A model trained on diverse data is adaptable, reliable, and capable of providing meaningful information in a variety of healthcare settings. In the long run, it is this kind of robustness that will make AI a reliable tool, not just for clinicians but for the entire healthcare system. With the right data, AI can truly live up to its potential as a valuable partner in medicine. But AI is a long-term game, and healthcare leaders should treat it as such.
Looking beyond today’s needs
One area where I see AI fundamentally reshaping healthcare in the future is predictive modeling. When you look at thousands of patient cases, patterns emerge that help us predict what’s coming. With AI, healthcare teams aren’t just reacting to what’s happening now. They can also predict what’s around the corner.
Artificial intelligence is helping pave the way for more proactive care.
For hospitals, this is invaluable. Knowing which patients are at higher risk allows them to closely monitor patients and allocate resources before a crisis strikes. This is not just a clinical improvement. it is practical, economical. Preventing emergency visits, reducing hospital stays and targeting care early means fewer high-cost interventions. It is efficiency that benefits patients and healthcare providers.
Connecting the data dots for precision medicine
Precision medicine is about understanding the whole person. AI multimodal capabilities make this approach a real possibility by combining disparate data sources—imaging, genetics, EHR, and more—into a coherent patient profile. With artificial intelligence, clinicians can see connections between these data points that would be nearly impossible otherwise.
And the value of AI extends far beyond acute care. For patients with chronic conditions, such as cancer or heart disease, long-term management requires constant adjustments. Artificial intelligence can help these patients by tracking disease progression and highlighting important changes. This is a critical benefit in my opinion because it has been found that early detection increase the effectiveness of pharmaceutical interventions for many types of cancer.
An expanding role in diagnostics and more
As I look to the future, I see AI becoming a core part of healthcare, embedded at every stage of the patient journey. From early diagnosis to post-treatment follow-up, AI has the power to support clinicians in making complex decisions, optimize care pathways and even predict patient needs before they arise.
Take a case of brain aneurysm. Here, artificial intelligence analyzes not only the growth rate of the aneurysm but also factors such as genetic markers and historical health data. This gives a complete picture of the patient’s unique risk profile, allowing the healthcare team to develop a highly customized treatment plan.
For clinicians, AI is a powerful tool to act on the full spectrum of each patient’s health. AI doesn’t just summarize reports or correlate data—it can quickly give clinicians a much deeper understanding of their patients. For patients, it means they receive a level of care that feels focused on them, because they are.
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