How AI is Changing Cancer Treatment

Unlocking new possibilities for precision medicine.

Key Takeaways

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.

Original article @ Medical Economics. 

The post How AI is Changing Cancer Treatment appeared first on Faisal Hoque.

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The Case

Northrop Grumman (NG) is a collection of more than 400 acquired companies that serves as a public and defense contractor. NG needed to assess, design, and create next generation organizational and client services capabilities with greater efficiency.

Approach | Output | Result

Shadoka team enabled the framework, processes, and data analytics needed to create repeatable and efficient management structure for:

  • 360° organizational and management capability assessments
  • On-going creation of internal and external business architecture that drove innovation and growth
  • Measuring and tracking value creation from internal operations and external impact

Supply Chain Resilience

The disruptions caused by geopolitical events, trade wars, the COVID-19 pandemic, and natural disasters have forced organizations to rethink their supply chain strategies. The traditional reliance on globalized supply chains is being challenged, with many companies opting for diversified, nearshore, or onshore suppliers to reduce risk. Technological solutions like blockchain are being integrated into supply chains to increase transparency and security, while IoT sensors allow for real-time monitoring of goods and inventory. Companies are also prioritizing sustainability in their supply chains, using eco-friendly sourcing and reducing their carbon footprints.

Examples: Companies using blockchain for product tracking, nearshoring manufacturing facilities to reduce supply chain risk, and improving logistics efficiency with AI-driven predictive analytics.

Transformational Leadership

We implement transformational leadership capabilities by emphasizing empathy, mindfulness, and systems thinking. Our research and practice advocates for leaders to develop self-awareness, emotional intelligence, and a holistic view of interconnected systems. We contrast this approach with traditional transactional leadership, focusing instead on inspiring and motivating others to innovate and create positive change. We encourage leaders to balance analytical thinking with emotional intelligence, foster creativity, and adapt to uncertainty. We architect capabilities for long-term vision creation, collaboration, and sustainable value creation. By integrating these elements, our approach aims to develop leaders who can effectively guide organizations through revolutionary global changes while fostering innovation and growth.

Innovation Management

We enable organizations to foster a culture of innovation, from ideation to execution with a robust innovation portfolio, ensuring that repeatable innovative practices are embedded in every aspect of business operations. We enable continuous creativity, and innovation by infusing cross-functional, cross-domain best practices based on research and practice. To us it is about setting up systemic processes that enables both micro and macro innovation as an ongoing journey. By promoting a holistic view that incorporates mindfulness and sustainability, our methods guide leadership in aligning short-term initiatives with long-term value creation and fostering collaboration across departments.

AI and Digital Transformation

We enable organizations with strong leadership, cultural change, strategic management of technology, and ethical governance. Our holistic approach such as O.P.E.N. and C.A.R.E. methodologies ensures that organizations not only adopt new technologies but also align them with their strategic goals and cultural values, thereby maximizing the benefits of AI and digital transformation. We address the ethical considerations of AI and digital transformation. We advocate for establishing AI governance frameworks to ensure ethical behavior and mitigate risks associated with AI systems. This includes creating governing bodies with diverse expertise to oversee ethical practices and communicate the importance of ethics throughout the organization.

Transforming Business and Technology Offices

We enable business and technology offices (i.e. Chief Information, Technology, Innovation, and Transformation offices) to shift their focus of tactical operations to strategic business leadership. Our management framework encourages these organizations to balance operational excellence with innovation, organizational adaptability, and data driven decision-making. This comprehensive approach helps evolve the leadership roles from a technical position to that of a strategic business partner and change agent, better equipped to navigate the challenges of the digital era and contribute to the organization’s long-term success. We emphasize a holistic approach that integrates Strategy Plan, Life Cycle Management, Program and Investment Management, Enterprise Architecture (EA), and Transparent Governance.

Digital Transformation and AI Integration

The digital transformation wave is redefining industries across the globe. Businesses are increasingly adopting advanced technologies like AI, machine learning (ML), blockchain, and the Internet of Things (IoT). AI-powered tools are now central to enhancing customer experience through personalization, predictive analytics, and real-time problem-solving. Automation in manufacturing, customer service, and operations is driving efficiencies, reducing costs, and enhancing output. Cloud computing and edge computing are enabling greater scalability and flexibility, making it easier for organizations to manage data securely. Companies that fail to embrace digital transformation risk losing their competitive edge as customers and markets increasingly demand tech-driven innovation.

Examples: AI-driven supply chain management, robotic process automation (RPA) in administrative tasks, and cloud-based collaboration tools are revolutionizing work processes globally.

Sustainability and Climate Action

Sustainability is now a top priority for businesses, governments, and consumers. As the effects of climate change become more evident, stakeholders are pushing companies to embrace environmental responsibility. The rise of green technologies—such as renewable energy sources (solar, wind, and hydroelectric power), electric vehicles, and carbon capture—is reshaping industries from energy to transportation. Consumers are demanding transparency regarding sustainable practices, leading to the rise of eco-friendly products, green packaging, and the shift toward circular economies where materials are reused, recycled, or repurposed to minimize waste. Investors are increasingly focusing on ESG criteria, pushing companies to address environmental, social, and governance challenges.

Examples: Companies setting carbon-neutral targets, sustainable supply chains, and the growth of green finance markets.

Shift in Global Demographics

Demographic shifts are having a profound impact on economies, labor markets, and consumer trends. In developed economies such as Europe, Japan, and North America, aging populations are leading to increased demand for healthcare services, retirement planning, and age-related products. This is also placing strains on the labor force, with some sectors experiencing shortages of younger workers. Meanwhile, emerging markets in Africa, Asia, and Latin America are witnessing a youth boom, with growing numbers of young, tech-savvy consumers who are reshaping markets. As a result, businesses must tailor their products and services to cater to both aging populations and younger consumers with different expectations and spending patterns.

Examples: Increased investment in healthcare technologies for aging populations, mobile-first services for young consumers in emerging markets, and the rise of the gig economy to supplement labor shortages.

The Case

PepsiCo has primarily grown through M&A activities. They needed to create a federated governance model that allowed them to create centralized reusable initiatives, investments, and processes for maximum ROI while maintaining individual brand operations.

Approach | Output | Result

Shadoka team enabled the framework, processes, and data analytics needed to create repeatable and efficient management structure for:

  • Recurring cost reduction of $500M annually
  • Increased speed of innovation and growth
  • Continuous and consistent management transparency from boardroom to program execution

E-commerce and Changing Consumer Behavior

E-commerce is now a dominant force in global retail, accelerated by the pandemic. Consumers expect on-demand, personalized, and seamless omnichannel experiences, where they can move effortlessly between online and physical stores. The demand for fast delivery, easy returns, and instant access to products has led to innovations in logistics and last-mile delivery. Consumer behavior is shifting toward experience-driven consumption, where customers prioritize experiences and brands that align with their personal values, such as sustainability or social justice. The rise of social commerce, where purchases are made directly through social media platforms, is also transforming how businesses engage with consumers.

Examples: The growth of direct-to-consumer (DTC) brands, subscription-based models, and the use of AI to provide personalized shopping experiences.

Geopolitical and Economic Uncertainty

The global economic landscape is being reshaped by geopolitical tensions, trade disputes, and shifts in global power dynamics. The rise of protectionism, trade wars, and tariffs has led to increased uncertainty, forcing businesses to rethink their supply chain and market strategies. Globalization is being recalibrated, with more emphasis on regionalization and localization to mitigate risks. Political instability in key regions, such as the U.S.-China trade tensions and the Russia-Ukraine conflict, adds further complexity. Additionally, economic volatility, such as inflation, fluctuating currency values, and rising interest rates, is impacting consumer spending and business investments.

Examples: Companies diversifying market strategies, establishing regional hubs to minimize geopolitical risks, and increasing investments in risk management and forecasting.

Learn

Study the current organizational landscape, market trends, and drivers of change.

Activities

This phase involves studying market trends, technological advancements, and the specific needs of the organization. It includes understanding the potential of AI and digital technologies in the context of the business.

Outcome

A well-rounded understanding of how digital and AI technologies can be leveraged to address business challenges and opportunities.

Investigate

Conduct thorough analysis of specific areas requiring transformation.

Activities

Conduct thorough investigations into existing processes, systems, and data infrastructure. This may involve data analysis, stakeholder interviews, and benchmarking against industry standards.

Outcome

Identification of key areas where digital and AI technologies can have the most significant impact, along with a clear understanding of the organization’s current capabilities and gaps.

Formulate

Develop a comprehensive transformation strategy and action plan.

Activities

Formulate actionable strategies and roadmaps based on insights gained from the Learn and Investigate phases. This includes setting clear objectives, defining key performance indicators (KPIs), and outlining the steps needed to implement digital and AI solutions.

Outcome

A comprehensive and actionable transformation plan that aligns with the organization’s goals and leverages digital and AI technologies effectively.

Take Off

Implement the transformation plan using an agile approach.

Activities

Execute the strategies and initiatives outlined in the Formulate phase. This involves mobilizing resources, engaging stakeholders, and ensuring effective communication throughout the organization. It also includes deploying AI models, integrating digital tools, and automating processes.

Outcome

The successful launch of digital and AI initiatives, with ongoing monitoring and adjustment to ensure alignment with strategic goals.

Study

Continuously evaluate outcomes and refine the strategy.

Activities

Assess the impact of the digital and AI initiatives against the defined KPIs. This includes collecting feedback, analyzing results, and identifying lessons learned.

Outcome

A thorough understanding of what worked and what didn’t, providing valuable insights for continuous improvement and future digital and AI transformations.

Unprecedented Global Challenges and Opportunities

We transform organizations by advocating for a holistic approach that integrates strong leadership, cultural change, business process management, and ethical governance. Our frameworks, such as the L.I.F.T.S. (Learn, Investigate, Formulate, Take Off, and Study), provide practical steps for organizations to navigate the complexities of change, uncertainty, and risk, ensuring that they not only adopt new models, capabilities, and technologies but also align them with their strategic goals and cultural values, thereby maximizing the benefits of transformation efforts. We equip teams with the tools and mindset to adapt and thrive in the face of adversity and rapid change.