Michael is a 45-year-old man who has just been diagnosed with colorectal cancer. He’s worried, scared, and feeling overwhelmed by the information he’s been given. He’s also worried about how he’s going to pay for his treatment. Thankfully, his doctor is able to use AI to help triage patients like Michael, to help him quickly and accurately diagnose his cancer, and to create a treatment plan that will give him the best chance of survival.
Michael is relieved that his doctor is able to use AI to help him make these important decisions. He knows that his doctor is able to access a wealth of information that would be impossible for him to process on his own. He also knows that AI is able to help him identify patterns in his health data that would be invisible to the human eye. Michael’s treatment is a success, and he’s able to return to his everyday life. His doctor is able to monitor his progress using AI, and he’s able to quickly identify any potential problems before they become serious. Michael is grateful for the role that AI has played in his recovery.
As Michael’s story shows, AI is playing an increasingly important role in healthcare delivery. It’s helping doctors to triage, diagnose, and treat patients more quickly and accurately. It’s also helping patients like Michael to get the care they need when they need it. But it’s important to remember that AI is only as good as the data it’s given. That’s why it’s so important to involve patients, doctors, and other stakeholders in the design of AI-driven healthcare systems. By doing this, we can ensure that these systems meet the needs of all stakeholders, including patients like Michael.
As we look to the future, we see that AI has the potential to transform healthcare delivery. But to realize this potential, we must design these systems with a human-centered approach. We must ensure that patients like Michael are at the center of everything we do.
According to OncoAlert, a worldwide network of Oncology professionals and patient advocates, colorectal cancer is now the leading cause of cancer-related death among males aged 20-39 and 40-49. It was third among 20-39 age group last year. A concerning trend is also seen in young women; CRC-related death will likely surpass breast cancer in the near future.
Why focus on colorectal cancer?
The American Cancer Society estimates that in total, there will be approximately 1,958,310 new cancer cases in the U.S. in 2023, the equivalent of about 5370 cases each day.
Colorectal cancer, commonly referred to as CRC, is a leading type of cancer that affects both men and women, but mostly older adults. However, there has been a recent trend of increasing incidence among young adults, especially men. This means that more and more young men are being diagnosed with CRC, which is a significant concern as it is a preventable disease if caught early. The exact reasons for this trend are not fully understood, but there are several potential explanations:
- Lifestyle factors: Young men are more likely to have unhealthy lifestyles, such as poor diet, lack of physical activity, and smoking, which can increase the risk of colorectal cancer.
- Increased obesity: The obesity epidemic has led to a higher incidence of colorectal cancer in younger adults, particularly men. Obesity is a known risk factor for CRC.
- Genetic factors: Some people are at a higher risk of developing colorectal cancer because of inherited genetic mutations, such as those in the APC gene.
- Lack of screening: Young men are less likely to be screened for colorectal cancer than older adults, which can lead to a delay in diagnosis and treatment.
- Inflammatory Bowel Diseases (IBD): Having a history of inflammatory bowel disease such as ulcerative colitis or Crohn’s disease also increases the risk of developing colorectal cancer, especially in young men.
What can be done?
Healthcare is an industry that touches all of us, given the most basic of human goals: we all want to lead healthy lives. Digital transformation has the potential to improve the screening process for colorectal cancer by increasing accessibility, improving accuracy and efficiency, and enabling personalized screening plans. It also plays a crucial role in early detection and cost-effectiveness.
With digital transformation, Michael can now easily complete his colorectal cancer screening from the comfort of his own home, eliminating the need for a trip to the clinic. He starts by filling out an online questionnaire provided by his doctor that assesses his risk for colorectal cancer based on his age, family history, and other factors. The results are then analyzed by a computer program that uses AI to identify patterns and predict risk. Michael’s risk is considered to be moderate and he is advised to go through a remote screening procedure, which includes a kit that he can use to collect a sample at home and then ship it back to the lab. The lab analyzes the sample and shares the results with Michael’s doctor, who can then access them via an EHR system. His doctor is able to quickly review the results and, thanks to the use of AI in the analysis process, any early signs of cancer are identified. Michael’s doctor is able to give him accurate and personalized recommendations for the next steps, reducing the chances of missed diagnoses.
Unfortunately, the complexity and uncertainty that envelops the cancer experience are all too familiar for many of us.
The healthcare industry is at the forefront of a technological revolution, with vast amounts of data being generated daily. However, this explosion of information can be overwhelming for healthcare professionals, who are struggling to keep up with the demands of IT systems. Meanwhile, colorectal cancer continues to be one of the leading causes of death worldwide, with a crude mortality rate of 12.0 per 100,000 inhabitants. Early detection of glandular tissue (adenomatous polyps) is crucial for preventing it. Colonoscopy is the recommended screening test for early cancer and adenomatous polyps, but it has limitations such as missing smaller and flat polyps during visual inspection.
This is where AI comes in. It has the potential to revolutionize the way we diagnose and treat colorectal cancer. We must take action and invest in AI technology to improve the accuracy of automatic polyp detection and classification as a preventive method for CRC, and to give healthcare professionals the tools they need to provide the best possible care for their patients.
Colorectal cancer is a highly preventable disease
For Michael, colorectal cancer is a highly preventable disease, and the use of AI in routine screening could be a game changer in decreasing the incidence of this type of cancer. Thanks to the advancements in technology, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas, which are precursors of colorectal cancer. Additionally, new treatments options like robotic surgery and computer-assisted drug delivery techniques are now available for Michael. Furthermore, the healthcare industry is moving towards precision or personalized medicine, and machine learning models have the potential to contribute to individual-based cancer care, transforming the future of medicine for Michael and many others.
The best human-centered AI design for Michael would take into account his unique needs and preferences as a patient, as well as the needs of his healthcare team.
Human-centered AI design in CRC care delivery
Artificial Intelligence has the tremendous potential to produce progress and innovation in society. Designing AI for people has been expressed as essential for societal well-being and the common good. However, human-centered is often used generically without any commitment to a philosophy or overarching approach. Human-centered design requires viewing humans as people. People with different prior experiences, needs, desires, ambitions, interests, irrational decision-making, and lifestyles embedded within specific cultural contexts. It is a shift of viewing humans not as a part of the system but as central in every aspect of the design.
In “Human-centered AI” Jan Auernhammer presents a comprehensive design approach that includes rationalistic AI development, humanistic AI design, and legal guidelines for new technologies, based on the examination of human impact through Human-centered Design Research.
Some of the key elements that could be considered in a human-centered AI design for Michael are:
- Patient input: Michael should be involved in the design process and have the opportunity to provide feedback on the AI system’s usability and effectiveness.
- Personalization: The AI system should be able to personalize the information it provides to Michael based on his specific condition, treatment plan, and preferences.
- Accessibility: The AI system should be easy for Michael to use and understand, with clear instructions and a user-friendly interface.
- Transparency: The AI system should be transparent in its decision-making process, allowing Michael and his healthcare team to understand how the system arrived at its conclusions.
- Integration with healthcare team: The AI system should be able to integrate with Michael’s healthcare team, providing them with real-time information and supporting their decision-making.
- Security: The AI system should have robust security measures in place to protect Michael’s personal and medical information.
- Explainability: The AI system should have the ability to explain its decision to Michael and his healthcare team, allowing them to understand the reasoning behind the AI’s recommendations.
- Continuous monitoring and improvement: The AI system should be continuously monitored and improved based on feedback from Michael and his healthcare team to ensure it remains effective and relevant over time.
Overall, a human-centered AI design for Michael should be tailored to his specific needs and preferences, with a focus on improving his overall experience and outcomes.
Designing for human-AI interaction, utilizing persuasive technology and a human-centered approach allows for the examination of how people interact and behave with the AI system.
By continually evaluating and improving these interactions, potential harm can be identified and prevented, resulting in a safe and secure experience for users.
