Niels Bohr, a pioneer of quantum theory, proposed that at fundamental levels “physical reality only takes shape upon being observed” (Reality, Humanity & AI | Issue 162 | Philosophy Now). In other words, measurement is not a passive mirror of an independent reality – it actively creates the observed outcome. This philosophy suggests that what we consider “reality” in an experiment depends on how we measure it. Applying Bohr’s insight beyond physics, we can infer that our understanding of any system (even a patient’s condition) is shaped by the instruments and frameworks we use to observe it. In medicine, diagnostic tools and tests thus don’t just reveal an underlying truth; they help construct the clinical reality by the way their results influence perception and decisions.
In clinical decision support systems (CDSS), AI models act as sophisticated measurement instruments that interpret patient data and output a prediction or recommendation. Crucially, these AI “measurements” are not neutral reflections of medical truth, but interpretations shaped by the data and algorithms behind them. Just as Bohr suggested measurement apparatus defines what is observed, an AI’s output defines how clinicians understand a case. For example, an AI risk score or diagnosis suggestion can frame a clinician’s thinking – highlighting certain possibilities and downplaying others – thereby shaping the perceived reality of the patient’s condition.
This means AI models in healthcare function like diagnostic tests: each has a certain sensitivity and specificity and may introduce biases. Indeed, AI tools have been called a “double-edged sword,” capable of improving diagnostic decisions for some patients and worsening them for others if the underlying data are biased (Clinicians could be fooled by biased AI, despite explanations - Michigan Engineering News). The model doesn’t simply reveal whether a patient truly has a disease; it infers it based on patterns it learned. Those patterns may include unwanted biases or proxies. For instance, if historical data underdiagnosed heart failure in women, an AI might learn to assign lower risk scores to female patients – not because women have less heart failure biologically, but because of biased past measurements (Clinicians could be fooled by biased AI, despite explanations - Michigan Engineering News). Such an AI would output “measurements” that systematically skew reality, potentially causing clinicians to under-diagnose women. In this way, the AI’s suggestion actively influences clinical interpretation rather than passively reflecting the patient’s true status.
Clinicians must therefore approach AI outputs as useful but partial views of reality. Blindly accepting an AI’s recommendation carries the danger of what informatics experts call automation bias – the tendency to trust a computer suggestion even when it may be flawed. Studies show that less-experienced clinicians are often most susceptible to this bias (Automation Bias in AI-Decision Support: Results from an Empirical ...). On the other hand, outright distrust of AI means losing potential benefits. Striking the right balance requires understanding that AI models, like measurement devices, have inherent limitations. They encode the perspective of their training data and design: what you get out is conditioned by how and on what the AI was “measuring” during development.
Modern AI, especially deep learning, relies on multiple layers of abstraction to derive its predictions. In deep neural networks, data is processed through many hidden layers that extract higher-order features from raw inputs ( The practical implementation of artificial intelligence technologies in medicine - PMC). Each layer transforms the data into more abstract representations – essentially creating derived “measurements” of the input. This layered abstraction lets AI detect subtle, complex patterns that human clinicians might miss, potentially improving sensitivity (the ability to catch true positives). However, it also means the model’s reasoning can be opaque, and it may pick up on spurious correlations not truly related to the disease (affecting specificity and causing false positives).
In practice, these effects have been observed in clinical AI studies. For example, a systematic review of AI assisting radiologists in lung nodule detection found that AI support increased radiologists’ sensitivity but slightly lowered specificity (The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review - PubMed). The AI’s multi-layer pattern recognition helped flag more true nodules (fewer misses), but it also introduced some false alarms. In other words, the AI’s abstracted “measurement” of the imaging data made radiologists more sensitive to possible cancer, at the cost of interpreting some benign findings as malignant. If clinicians are aware of this trade-off, they can adjust their thresholds or follow-up strategies accordingly. The key is recognizing that the AI’s output is an augmented view of reality – one that might lean towards over-calling findings in order to catch more disease.
On the flip side, an AI model’s layered abstraction can also miss context that a human would catch, reducing sensitivity in unexpected ways. A striking real-world example is the Epic Sepsis Model (ESM), an AI-based warning system for sepsis risk. ESM was developed and validated on retrospective hospital data, but when deployed it performed far worse than expected – its sensitivity and specificity dropped so much that it missed two-thirds of sepsis cases while frequently sounding false alarms ( Bias in medical AI: Implications for clinical decision-making - PMC). The measurement it provided (a sepsis risk score) did not mirror the true incidence of sepsis in practice. Why? The layers of abstraction had likely latched onto patterns that didn’t generalize (for instance, proxy variables in the training hospital that were not predictive elsewhere). This spectrum bias – where an algorithm derived on one population loses accuracy in another – meant the tool’s “reality” was misaligned with actual patient reality (Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms | CoLab) (Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms | CoLab). Clinicians following ESM’s alerts faced many unnecessary workups and missed real sepsis cases, illustrating how an AI’s measurement can mislead. Notably, ESM’s deployment led to increased diagnostic testing and treatments triggered by false positives, ultimately lowering quality of care and increasing costs ( Bias in medical AI: Implications for clinical decision-making - PMC ). Such outcomes erode trust in the system.
These cases underscore that sensitivity and specificity are not inherent constants of an AI tool – they depend on the context and how the model’s abstractions align with reality. An AI may boast high accuracy in a controlled setting but fail in the wild if its layered logic doesn’t hold. For clinical practitioners, it’s critical to validate that an AI’s “measurements” of patient data truly correlate with clinical reality in their setting. If an AI has multiple layers distilling an X-ray image into a diagnosis, we must ask: are those layers picking up medically meaningful signals, or artifacts? The answer will directly impact the model’s real sensitivity (does it catch the disease when present?) and specificity (does it avoid false alarms?).
The multi-layered complexity of AI models often makes them black boxes, where even experts cannot fully trace how inputs lead to outputs. This opacity creates a serious trust dilemma in clinical settings. Clinicians are taught to understand the rationale behind a decision or test result; with AI, that rationale can be hidden in thousands of neural network weights. As a result, providers may be unsure when to trust an AI recommendation and when to follow their own judgment (). Bohr’s lesson that measurements shape reality becomes worrisome here – if the “measurement apparatus” (AI) is inscrutable, how comfortable are we letting it shape our clinical reality?
Lack of transparency has been cited as an important barrier to CDSS adoption ( To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems - PMC ). Physicians might hesitate to use an AI-driven suggestion without knowing why it’s suggesting something, especially if it contradicts clinical intuition or guidelines ( To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems - PMC ). This initial skepticism is healthy to a point, preventing blind faith in unproven tools. However, too much skepticism can lead to disregarding useful AI advice altogether, negating potential benefits. The goal is calibrated trust – believing the AI when it has shown to be reliable, but verifying or overriding it when it’s likely off base.
One approach to improve trust is adding explainability to AI systems. By providing reasons or visual highlights for an AI’s prediction (for example, indicating which sections of an image influenced a diagnosis), developers hope clinicians can judge whether the AI is “seeing” meaningful patterns or chasing noise ( To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems - PMC ) (). Regulatory bodies like the FDA are advocating for such transparency; the FDA’s draft guidance for AI in healthcare calls for making model logic as transparent as possible (Clinicians could be fooled by biased AI, despite explanations - Michigan Engineering News) (). The idea is that if a physician can peek into the AI’s reasoning, they can better decide when to trust it.
Yet, explanations alone do not guarantee trust or safety. A recent study published in JAMA tested clinicians with an AI CDSS for diagnosing respiratory failure, providing some with explanations (heatmaps on chest X-rays) () (). Alarmingly, when the AI model was intentionally biased – for example, overestimating pneumonia in older patients – clinicians often failed to catch the error even with the explanation. In fact, their diagnostic accuracy dropped from 73% without AI to 62% with the biased AI’s assistance, because they trusted its skewed suggestion (). The added explainability (which showed the AI focusing on age or other factors) did not sufficiently alert clinicians to the bias. This outcome highlights that explanations can help but are not a panacea; if the underlying model is biased or wrong, an elegant explanation might just lend false confidence.
The trust dilemma, therefore, has two sides: over-trust (automation bias) and under-trust. Over-trust can lead to errors when the AI is wrong (as seen in the biased model case), while under-trust means missing out on help when the AI is right (as seen when doctors dismiss correct AI alarms that “don’t fit” their expectation). Building trust in AI-assisted medicine requires more than opening the black box – it demands rigorous evidence that the AI is reliable in the first place, and user training so clinicians understand both the capabilities and limitations of the tool. In essence, clinicians need to view AI recommendations as one source of evidence among many, to be weighed according to its proven accuracy and understood scope.
To safely integrate AI “measurements” into clinical reality, strong validation and bias mitigation practices are essential. Before an AI model ever influences patient care, it should undergo extensive testing on real-world cases and diverse populations. This is akin to calibrating a new lab instrument – we must ensure it measures correctly across the range of expected conditions. Researchers recommend conducting prospective clinical trials for AI systems, just as we do for new drugs or devices, to confirm performance and uncover hidden biases ( Bias in medical AI: Implications for clinical decision-making - PMC ). Regulators are moving in this direction: the FDA’s Software as a Medical Device (SaMD) guidelines emphasize robustness, and experts argue they should extend to explicitly check for fairness and bias in AI outputs ( Bias in medical AI: Implications for clinical decision-making - PMC ) ( Bias in medical AI: Implications for clinical decision-making - PMC ). An AI that works well in one hospital or demographic group should not be assumed to work for all; sensitivity and specificity must be re-assessed in each new context. If performance degrades (as with the sepsis model or a spectrum bias scenario), the AI should be tuned or retrained for the local setting before it is trusted.
Bias mitigation must be addressed at every stage of the AI lifecycle ( Bias in medical AI: Implications for clinical decision-making - PMC ) ( Bias in medical AI: Implications for clinical decision-making - PMC ). This starts with training data: if the data is unbalanced or captures historical inequities, the model will likely inherit those. Developers should use datasets that are representative of the patient population and include relevant social determinants, to avoid blind spots ( Bias in medical AI: Implications for clinical decision-making - PMC ). It’s also critical to have clinicians and domain experts involved in labeling data and designing features, so that implicit biases (for example, labeling based on substandard care practices of the past) can be caught and corrected ( Bias in medical AI: Implications for clinical decision-making - PMC ). During model development, teams need to look beyond overall accuracy and examine performance across subgroups – a model that has 90% sensitivity overall might be only 70% for a certain minority group, which is unacceptable. Techniques like equalized odds (ensuring similar sensitivity/specificity across groups) can be applied if disparities are found ( Bias in medical AI: Implications for clinical decision-making - PMC ).