How AI is changing cancer treatment

AI improving cancer treatment: ©Summit Art - stock.adobe.com

Cancer is a deeply personal subject for me. My world was shaken when my son was diagnosed with a rare form of the disease. I witnessed how complex and unpredictable the journey could be, from the difficulty of diagnosing to finding the right treatments. But I am also witnessing how innovation in medical technology playing a role in his treatment.

These days, I often find myself investigating any new forms of treatment that may be of value for patients like him. That’s just one driving force behind my enthusiasm for the growing use of AI in the development and efficacy of immunotherapy.

Today, artificial intelligence is providing those tools, transforming how we diagnose, treat, and manage cancer. AI’s potential to improve patient outcomes, tailor treatments, and reduce the burden on health care systems is immense. As someone who has witnessed the life-saving impact of medical innovation, I’m optimistic that AI can help many more families avoid the devastating uncertainty I once faced.

AI-powered diagnostics: A game changer in early detection

Accurate and early detection is often the key to successful cancer treatment. However, traditional diagnostic tools, including biopsies, mammograms, and imaging scans, have limitations. False negatives and positives remain a challenge, leading to delayed treatment or unnecessary procedures. AI is making major strides in addressing these gaps.

AI-powered diagnostic tools are designed to improve the accuracy of detecting abnormalities. For instance, algorithms trained on massive datasets of mammograms have achieved near-human or even superior accuracy in spotting early signs of breast cancer. Some studies have reported that AI systems can evaluate mammograms with 99% accuracy, a figure that can dramatically reduce misdiagnoses and improve early detection rates.

Consider the UK’s National Health Service (NHS), which is currently piloting the world’s largest AI trial for breast cancer detection, involving over 700,000 mammograms. This trial aims to compare AI’s efficiency with that of human radiologists and potentially pave the way for more cost-effective diagnostic protocols. By using AI, health care providers can process scans more quickly, identify high-risk cases, and prioritize patient care.

Early and accurate diagnosis has long-term financial implications. When cancer is caught early, treatment options are typically less aggressive and less expensive, reducing the burden on both patients and healthcare providers. Furthermore, AI’s ability to catch subtle signals in imaging data—something that human eyes may miss—offers a new level of precision in oncology care.

Personalized treatment plans: Moving beyond one-size-fits-all approaches

Cancer is a highly individualized disease. Two patients with the same type of cancer may respond to treatments in entirely different ways due to genetic differences, underlying health conditions, and other factors. The emergence of precision medicine has underscored the importance of creating tailored treatment plans, and AI is at the forefront of this effort.

AI algorithms can analyze vast amounts of patient-specific data, including genetic information, medical history, imaging scans, and previous treatment outcomes. By identifying patterns within this data, AI can help clinicians predict how a patient will respond to certain therapies. For example, some AI systems are being used to determine which cancer patients are most likely to benefit from immunotherapy, a promising but often unpredictable treatment option.

Immunotherapy works by harnessing the body’s immune system to fight cancer, but its effectiveness varies widely among patients. By applying AI to genomic and molecular data, oncologists can predict whether a specific patient’s immune system is likely to respond to the treatment. This prevents patients from undergoing expensive and potentially ineffective therapies, ultimately saving time and resources while improving outcomes.

AI also facilitates real-time adjustments to treatment plans. As new data becomes available—such as how a tumor is responding to initial treatment—AI systems can recommend modifications, ensuring that patients receive the most effective care at every stage of their cancer journey.

Cost savings through preventive care and proactive interventions

One of AI’s most promising contributions to oncology lies in its ability to predict and prevent adverse events before they escalate. AI systems can monitor patient health in real time, identifying patterns that may indicate a risk of complications or emergency care needs.

At the Center for Cancer and Blood Disorders in Texas, AI tools are being used to predict which patients are most likely to visit the emergency room within the next 30 days. By identifying at-risk patients early, health care providers can intervene with proactive measures, such as adjusting medications or scheduling follow-up visits. This approach has led to estimated cost savings of $3 million by reducing unnecessary hospital admissions and emergency room visits.

Preventive care not only benefits healthcare systems financially but also improve patients’ quality of life. Fewer emergency visits mean less disruption to patients’ daily lives, reduced stress, and lower out-of-pocket costs. This shift toward preventive care, powered by AI, is an essential step in making cancer treatment more sustainable and patient centric.

Drug discovery and development: Profound impact

Perhaps the most profound impact of AI in oncology lies in drug development. Traditional cancer drug development takes an average of 10-12 years and costs upward of $2 billion per successful drug. AI is dramatically accelerating this process.

Insilico Medicine recently demonstrated the potential of AI in drug discovery by developing a novel cancer drug candidate in just 18 months at a fraction of the traditional cost. The company's AI system analyzed millions of potential molecules to identify promising candidates, then optimized them for efficacy and safety.

Challenges in implementing AI: Balancing innovation with practicality

Despite its many advantages, the integration of AI into cancer care is not without challenges. One major concern is the potential overreliance on technology at the expense of human oversight. Some experts caution that while AI can provide valuable insights, it is not a replacement for the expertise and judgment of oncologists.

For example, in the UK, experts have raised concerns about whether the NHS’s focus on technological solutions might lead to neglecting essential aspects of cancer care, such as timely referrals and personalized follow-ups. A balance must be struck between leveraging AI’s capabilities and ensuring that human clinicians remain actively involved in decision-making.

Another challenge lies in ensuring that AI algorithms are equitable and unbiased. Since AI systems learn from historical data, they may inadvertently perpetuate existing disparities in health care access and outcomes. For example, if an algorithm is trained primarily on data from affluent populations, it may not perform as effectively when applied to underserved or minority communities. Addressing this issue requires careful oversight and continuous evaluation to ensure that AI benefits all patients equally.

Finally, data privacy and security concerns must be addressed. Cancer patients’ medical records contain sensitive information, and any breach of this data could have serious consequences. Health care organizations must implement robust cybersecurity measures and adhere to strict data protection regulations to ensure patient trust.

The future of AI in oncology

AI’s potential in cancer treatment is immense, but its success will depend on how well we navigate its challenges. By combining AI’s data-driven precision with the compassion and expertise of human clinicians, we can achieve a new era of personalized, effective, and cost-efficient cancer care.

Looking ahead, continued investments in AI research and development are critical. Governments, private sector organizations, and research institutions must collaborate to create standardized protocols for AI use in oncology. These protocols should prioritize patient safety, equity, and ethical considerations while fostering innovation.

As AI systems become more advanced, they could help predict cancer risks before symptoms even appear, offering patients the chance to take preventive measures. Furthermore, AI’s role in drug discovery could lead to the development of more targeted and less toxic therapies, further improving patient outcomes.

Conclusion

AI is not a cure for cancer, but it is a powerful tool that can complement existing medical practices and improve outcomes for patients. By enhancing diagnostic accuracy, personalizing treatment plans, and reducing the costs associated with emergency care and ineffective treatments, AI is paving the way for a more efficient and patient-centered approach to oncology.

However, we must approach this technological revolution thoughtfully. Balancing innovation with practicality, addressing bias, and safeguarding patient data will be essential in ensuring that AI delivers on its promise of transforming cancer care for the better. The future of oncology is bright, and AI is lighting the path forward.

Faisal Hoque is recognized as one of the world's leading management thinkers and technologists. He is an award-winning entrepreneur and innovator, and a #1 Wall Street Journal and USA Today best-selling author. He is the founder of SHADOKA, NextChapter, and other companies. His next book,”TRANSCEND: Unlocking Humanity in the Age of AI” is scheduled to be released in April 2025 by Post Hill Press, and distributed by Simon & Schuster. Follow him @ faisalhoque.com.