Jurgi Camblong, PhD is the Co-Founder and CEO of SOPHiA GENETICS.
Treating cancer is becoming an increasingly sophisticated endeavor driven by technological advances and scientific discoveries. I think central to this progress is our deepening understanding of the human genome and the ability to pinpoint the genomic mutations at the root of various cancers.
Since the report of the first whole genome sequencing in 2003, genomic analysis has transformed from a futuristic concept into the gold standard of care. By examining the unique genetic makeup of a tumor, genomic testing identifies mutations driving cancer growth, empowering clinicians to tailor treatments specifically targeting these changes.
This approach can not only increase treatment effectiveness but also reduce unnecessary side effects, marking a pivotal shift toward more precise, individualized cancer care.
How AI Can Help Inform Care
The problem? Genomic analysis produces vast amounts of complex data—far too much for even the most skilled scientists to interpret effectively. Each tumor’s unique genetic code may reveal hundreds of mutations and interactions, all of which hold crucial clues for treatment.
This is where artificial intelligence (AI) can help clinicians by sifting through the terabytes of data, what I call finding the signal in the noise. From there, AI can help interpret that signal, identify patterns and correlate genetic mutations with potential therapies.
As noted earlier, AI has reshaped how we diagnose, treat and monitor cancer. By harnessing AI’s ability to analyze complex datasets and predict patient responses, oncologists can now move beyond a statistical treatment approach to a more individual approach, using advanced algorithms to recommend the best therapies from the start and ultimately improve patient outcomes.
Personalized Treatments
I believe such personalized treatment is crucial in cancer care because no two tumors are exactly alike, and therapies that work for one patient may be ineffective—or even harmful—for another. By tailoring treatments to the unique genetic profile of each tumor, doctors can target the cancer’s specific vulnerabilities, maximizing the chances of success while minimizing unnecessary toxicity and side effects.
Collaboration Among Experts
To drive the adoption of AI technologies in cancer care, I think it’s essential to foster a collaborative environment where technology and human expertise work seamlessly together. By leveraging distributed AI platforms that enable global knowledge sharing, clinicians can tap into the collective expertise of leading research institutions, accelerating the adoption of precision medicine worldwide.
Toward this, business leaders can focus on creating or adopting platforms that enable clinicians and researchers to share insights. By joining such networks, this can enhance their ability to detect genetic mutations with greater accuracy, amplifying their insights not only through the data-driven precision enabled by AI tools but also through community support and collaboration.
This networked approach accelerates the sharing of knowledge across institutions, helping to build a robust framework where AI and human expertise complement each other. When key learnings from data are pooled and shared effectively, clinicians can compare individual cases against a vast dataset, uncovering new solutions more rapidly than ever before.
Augmenting Expertise
To be clear, the role of AI isn’t to replace clinicians but to enhance their decision-making capabilities, helping to unearth insights that maximize the ability to target treatments for individual patients. By analyzing complex datasets and providing actionable insights, AI is a tool to augment a physician’s expertise. The final judgment and treatment decisions remain firmly in the clinician’s hands.
Healthcare technology companies using AI must recognize that their role is not to offer a cure-all but to provide tools that clinicians can trust and rely on. By positioning AI as a collaborative ally, helping to bring unseen insights to the surface to understand drivers of diseases, we can empower healthcare professionals to make informed decisions.
Greater Accessibility
The rise in cancer cases is not evenly shared among the world’s nations as low- and middle-income countries bear the brunt of the global cancer burden, with an overwhelming amount of fatalities due to cancer in low- and middle-income nations.
Healthcare facilities in these areas may lack the technology necessary to facilitate genomic testing and diagnostics, as well as robust monitoring capabilities. AI presents a remarkable opportunity to bridge the divide between those who are creating lifesaving diagnostic testing and cancer treatment options and those who need access to it.
Looking To The Future
Looking to the future, cancer care is now advancing from genomics to multiomics—the integration of diverse biological datasets, including clinical, biological, genomic and imaging data—to provide a more comprehensive understanding of a patient’s health. As we continue this evolution, it’s important that leaders democratize access to these insights, allowing every patient, regardless of geography, to benefit from the best possible care.
I believe the future of cancer care lies in the synergy between AI, clinical expertise and the collaboration between organizations and these tools. By embracing a new mindset, we can close the gap in cancer outcomes and ensure that every patient has access to the highest standard of care.
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