The Problem of Early Diagnosis
Returning to healthcare scenarios, after a full exam week focused solely on philosophy and its many-layered reflections, I now feel richer for it and ready to dive back into the interdisciplinary field of AI ethics in healthcare. Let’s step into someone else’s shoes for a moment. The feelings this person feels, what they have to put on hold, and how they plan to move forward after receiving difficult news from a doctor. Here’s the scenario:
You’re sitting in a doctor’s office, but not too comfortably. This time, the tension in the room is unmistakable. You sense that you’re about to hear something serious. Trying to ease things for both yourself and the doctor, you ask, "Is it bad?" The doctor answers, “Unfortunately, yes, it is. You have a…” Fill in the blank. This moment will come for most of us one day. Some might have more time waiting, others a little less.
One might feel fortunate to find out early, while another would have preferred to fully enjoy life, unaware of what was coming. Receiving a diagnosis today for a condition that might not manifest for another twenty years is devastating.
With current advancements in AI, this scenario is already true within the research field. It raises a question that healthcare professionals, ethicists, and technologists are only beginning to explore: how early is too early to know? While AI’s ability to forecast health conditions holds life-saving potential in curable diseases, its implications in large-scale patient care require a thoughtful, balanced approach to diagnosis and treatment plan development.
Should we approach curable and non-curable diseases differently? Can we exclude certain diseases from early diagnostics, or should we? Would it be a personal choice for the patient to decide if they want to know decades in advance, and if so, could they later change this choice? If patients change their minds later, will they have access to previously detected conditions from routine tests?
Or should we only test for conditions with the patient’s explicit consent? And if an incurable disease is discovered while examining for a curable condition, how do we ensure that this information doesn’t reach those who would prefer not to know?
The Right to Know—Or Not
Patient autonomy must remain central. Early diagnostic AI tools could alert patients to conditions that might emerge years later, from manageable ailments to those with severe impacts. But not everyone may want to know about a potential future illness, especially if that illness lacks effective treatment options.
What does autonomy entail? Autonomy is not only the right to receive timely information but also the right to control how much one wishes to know. For AI to be a tool for empowerment, patients should be able to decide when and if they wish to be informed of potential future conditions.
Consider Alzheimer’s disease: knowing about it years in advance may help some prepare, but it could also impose a psychological burden on others, who may feel they’re living under the shadow of inevitable decline. Some may even start to act according to their future diagnosis, which could be detrimental to both the individual and their family. Every normal forgetful moment would remind them of the inevitable.
Informed choice should be a foundational element in AI diagnostic tools, allowing individuals to opt-in or out based on their values, mental preparedness, and willingness to engage with this knowledge.
The Psychological Impact of Early Diagnosis
Early diagnosis can profoundly affect mental well-being. While empowering, it can sometimes lead to a “countdown mentality,” especially for diseases without cures. For a person in their 20s, 30s, or 40s, this foreknowledge can take away many years of productive work life due to fear or burnout from the overwhelming nature of the diagnosis.
Learning about a possible future illness could give individuals more time to shape their lives with greater meaning and purpose. They might choose to prioritize relationships, pursue passions, or make impactful changes knowing what lies ahead.
However, this outcome isn’t guaranteed; if a person isn’t ready to handle the weight of this information, it may not bring anticipated clarity or purpose. Instead, it could lead to feelings of fear, overwhelm, or burnout.
This delicate balance, between using foreknowledge to enrich one’s life and risking psychological strain, is crucial in considering how and when early diagnoses are delivered. This information can indeed give individuals more time to make their lives meaningful. But shouldn’t we strive to create meaningful lives without needing a diagnosis?
For a young person, an illness that might develop in their 70s may not feel as daunting, especially since data already shows that certain health conditions are inevitable over time. But think, if the illness could emerge in their 40s or 50s, how might we handle it differently? This knowledge could drive anxiety, overshadow everyday joys, or lead to a self-fulfilling prophecy where patients feel they are simply “waiting” for symptoms to appear.
Studies in psychology show that individuals often experience anticipatory grief when informed of likely future health issues. The mental toll of this foreknowledge can impact quality of life, sometimes leading to depression or decreased overall well-being. A study on Huntington’s disease, a genetic disorder with no cure, found that some people regretted knowing their status, as the burden of awareness impacted their mental health more than the condition itself, especially in the early years when they felt asymptomatic.
Balancing psychological risks with potential benefits is essential for AI-driven diagnostics. AI systems might integrate assessments that gauge an individual’s mental readiness for early diagnosis or at least offer counseling resources to help patients process and cope with the information.
Ethical Implications for Providers and AI Developers
Healthcare providers and AI developers must collaborate to design AI tools that honor patient well-being, autonomy, and mental resilience. This raises some practical questions: When should the diagnostic results be shared? Should healthcare providers always have the final say in timing, or could patients decide?
What about children, how old is old enough to decide? Could this decision be changed, and if so, would the analyzed data already exist from previous blood tests, such as those for cholesterol or blood sugar in one’s 20s? Could you access this information from the comfort of your home, especially during anxious moments, certain that pain in your chest isn’t just from too much coffee, but must be heart disease?
You make the change, only to discover that, yes, it was probably just too much coffee... and that you also have early indicators of progressive Parkinson’s disease, estimated to show symptoms in 10 years.
Would there be an option to only see diagnoses that are actionable or curable through lifestyle choices? Would patients go through a list of diagnoses to decide which to exclude from their “to-know” list? Incorporating safeguards like phased notification protocols could help uphold patient autonomy and mental readiness.
For example, patients at low risk for disease progression might only be informed about lifestyle modifications rather than receiving a full diagnosis. Industry and healthcare providers must work together to define “ethical release thresholds,” where an individual’s consent, age, mental readiness, and the treat-ability of the condition together determine when and how diagnoses are disclosed.
Privacy is another key ethical concern. Diagnoses often become part of medical records, affecting future treatment decisions and potentially influencing health insurance eligibility. To prevent undue consequences, developers must ensure compliance with privacy laws like GDPR or HIPAA, using robust data protection protocols to safeguard patient information.
Balancing Early Intervention with Patient-Centered Care
The ability of AI to predict and diagnose conditions early offers undeniable benefits for preventive care, especially for conditions where timely intervention can be life-saving. For example, detecting early signs of heart disease or certain cancers enables patients and healthcare professionals to adopt preventive measures that could alter their health trajectories. The goal of AI in healthcare should be to support, not override, the values and mental well-being of patients.
Ideally, AI tools should provide flexible intervention plans tailored to each patient’s needs and readiness. For instance, in the case of cancer, AI systems could offer lifestyle recommendations without explicitly diagnosing a specific condition until certain progression criteria are met. This approach allows patients to engage in preventive care while avoiding the anxiety that may arise from knowing too much, too soon.
However, this raises important questions. Wouldn't a patient start to wonder, “Why was my dietary plan changed? What do they know that I don’t?” This highlights a fundamental issue with AI systems when they know more about a person than the individual does. A lack of control over decisions can undermine a patient’s sense of agency, which is crucial for empowerment.
While some individuals may appreciate not having to worry about every detail, finding a sense of freedom in deferring decisions to AI, this can also inadvertently lead to over-reliance on AI systems.
Psychological Support an Essential Companion
For patients receiving an early diagnosis, emotional and psychological support should be essential. Counseling should be readily available to help patients interpret and integrate their diagnosis into their lives in a manageable way.
Even with the best technology, healthcare remains a fundamentally human experience, and access to mental health resources is critical.
Additionally, access to reliable, comprehensive information that aids in understanding the complexities of one’s condition, without the need to search across various websites, is crucial. Current technology already enables 24/7 counseling support, reducing the need for patients to rely on internet discussion forums.
While these forums serve a purpose, it is vital that patients receive information from trusted, authoritative sources. AI systems could also incorporate tools to guide healthcare providers on best practices for delivering early diagnosis information sensitively. Training providers to approach these conversations with empathy and compassion could alleviate some of the stress for patients receiving difficult news.
Designing Ethical AI in Healthcare
Ethically implementing early diagnosis capabilities requires a multi-faceted approach. Healthcare providers, AI developers, and patients all play a role in setting standards that keep well-being at the forefront. Here are a few guiding principles:
Patient Choice and Consent: AI should allow patients to define their engagement with early diagnostics, providing options to customize notification timing.
Support and Counseling: AI-driven diagnoses should come with access to mental health support and resources.
Transparent Data Privacy: AI in healthcare must comply with privacy laws and protect patient information, ensuring that diagnoses do not impact non-clinical aspects of life without consent.
AI for Patient Empowerment, Not Just Prediction
AI holds the power to transform healthcare, and early diagnosis is one of its most promising applications. But with that promise comes the responsibility to respect patients’ autonomy and psychological needs. The goal of AI should be to empower individuals with choices, helping them engage with their health on their own terms.
As we move toward a future of AI-integrated healthcare, the question of “how early is too early?” must be carefully investigated. Early diagnosis can save lives, but only if it supports the well-being, dignity, and autonomy of those it serves.
Warmly,
Riikka
Reference:
Paulsen, J. S., Nance, M., Kim, J. I., Grow, J., Ross, C. A., & Shoulson, I. (2001). “Early Disease Progression in Huntington's Disease: A Study of Anticipatory Grief.” American Journal of Psychiatry, 158(5), 742-747.
