This blog post delves deeply into whether perfect diagnosis and treatment are possible within the inherent uncertainty and limitations of medicine.
Human history has been a continuous struggle to find certainty amidst uncertainty. Humankind has strived to seek truth across diverse fields such as religion, philosophy, and science. Discussions on the Forms in ancient philosophy sought to understand the essence of things, while Newton’s laws in science represented an attempt to comprehend the laws of motion. One crucial commonality exists in this pursuit of truth: inference through rules. Long ago, humans discovered the rules governing periodic climate changes and used them to predict future weather, which greatly contributed to the transition from hunter-gatherer societies to agricultural societies.
‘Medicine’ is similar. Doctors have systematically classified countless pathological symptoms to define specific diseases and developed appropriate treatments based on past clinical experience. When a patient visits, the doctor infers diagnosis and treatment based on these rules. Thus, medicine is also a discipline that starts from uncertainty and pursues certainty. In this process, medicine still contains much uncertainty. However, people tend to view medicine as a refined, certain discipline rather than acknowledging its inherent uncertainties. For example, when a doctor tells a terminal cancer patient their remaining life expectancy, it is often misunderstood as an ‘exact time of death’. Yet the period mentioned by the doctor merely indicates a higher probability of death around that time on average; it does not mean every patient will die within that timeframe. Similarly, when a doctor explains various possibilities to a patient’s family after surgery, the family may demand, “Just tell us for sure whether the patient will live or die.”
Even in modern medicine, uncertainty persists, and there remains doubt about whether this uncertainty will ever be fully resolved. Paradoxically, as medicine advances, uncertainty actually increases. Diagnostic technologies improve, new drugs and treatments are developed, making disease diagnosis and treatment increasingly specialized and complex. Consequently, medical rules become entangled, increasing the difficulty of interpretation and application in diagnosis and treatment.
The reason medicine feels uncertain can also be found in its very definition. Medicine is “the discipline that studies the structure and function of the human body, the diverse phenomena of health and disease, and develops techniques for maintaining health, preventing disease, and treating illness.” The crucial points here are the words ‘human body’ and ‘treatment’. The human body is extremely complex, and every individual is different. While dealing with this complex human body, doctors must apply universal rules based on empirical statistics. However, even these universal rules are innumerable and diverse. This creates uncertainty about how to interpret and apply collective data to individual patients.
For example, stating that a specific anticancer drug is effective means “a percentage of patients showed effective results in clinical trials,” not that “it is effective for all patients.” Furthermore, having a specific disease does not guarantee that it will always follow the same progression. Similarly, even if a diagnosis of ‘appendicitis’ is made by combining several symptoms, there is no guarantee it is 100% appendicitis. In reality, while 60% of patients are likely to have appendicitis, the remaining 40% could potentially have other conditions.
Modern ‘evidence-based medicine’ is founded on reproducibility and universality. That is, the assumption that the same treatment yields the same results and can be applied anywhere. But can medicine, dominated by uncertainty, truly be called a science? Which parts of medicine are scientific, and which are unscientific?
First, consider the process of devising treatment methods. Treatment methods broadly fall into two categories. The first involves treatments that were empirically proven effective by chance and later validated through research. The second involves treatments developed by applying scientific theories to specific diseases. An example of the first is ‘aspirin,’ an anti-inflammatory analgesic documented in Egyptian papyrus from 1500 BC. The second example is anti-angiogenic drugs based on VEGF receptors, developed from research on tumor angiogenesis. The latter can be considered scientific because it was developed based on scientific principles. However, the former is difficult to definitively label as scientific since it relies on empirical statistics.
In the case of aspirin, it was initially used empirically, but its mechanism was later elucidated through scientific research. It was confirmed that aspirin inhibits the cyclooxygenase enzyme to alleviate inflammation and pain, and acts on the body’s temperature regulation center to produce an antipyretic effect. Therefore, aspirin can be considered a prime example of a treatment that originated from non-scientific experience but later acquired scientific evidence.
Similarly, uncertainty exists in the process of a doctor making a diagnosis and prescribing treatment. Specific symptoms can be indicative of multiple diseases, and the probability is never 100%. While doctors synthesize various symptoms and data to make a diagnosis, this process inevitably involves uncertainty. Furthermore, even for a disease with an approved treatment, it is not certain whether that treatment will be effective for a specific patient. Ultimately, even when treatment proceeds according to medical rules, synthesizing the entire process cannot be scientific, and the final judgment is made by imperfect humans.
Therefore, medicine can be called an ‘imperfect science’. Medicine pursues probabilistic diagnosis and treatment through rules, but due to the individual characteristics and complexity of each patient, a perfect scientific approach is often difficult.
