You can read the 2001 Netherlands study, "Relationships and Causation in Living Matter: Reframing Some Methods in Life Sciences?", that came to same conclusions as below here.

Chat GPT Summary

Life Is Not an Object, But a Process of Relationships

  • A living system is not reducible to its parts, its molecules, or its genes.
  • Life is a relational phenomenon, a continuous flow of coherence, not a fixed object to be dissected.
  • Therefore, it cannot be studied as a machine or a closed system. It must be understood as dissipative, relational, and emergent—always changing, always in contact with its environment.

Classical Causality Is Inadequate for Biology

  • Traditional cause-effect thinking (Newtonian determinism, billiard-ball models) cannot account for the dynamic, unpredictable, and non-linear behavior of living matter.
  • Life involves electrodynamic coherence, vacuum field fluctuations, and non-deterministic causation, meaning it operates through fields, resonance, and phase relationships that can’t be captured by reductionist logic.
  • Biological "laws" are not fixed; they are context-dependent and relationally emergent.

In Summary:

Life is not a machine. It is a coherent phase flow, born from vacuum symmetry, shaped by purpose, and expressed through field resonance.
Any science that tries to treat it as a deterministic object is doomed to fail, and worse—mislead.

Everything wrong with modern medicine can be summarized by saying this: we’re using tools and laws designed based off of inert material, engineered machines, to diagnose perceptive, responsive, capable living fields.

Scalar, mechanical testing models, built to assess stability, not coherence, are being used to interpret dynamic biological systems. They can’t do it. They were never designed to.

The human body is not a machine. It's not made of independent parts that either work or don’t. It’s a resonant, adaptive, intelligent field of electromagnetic, chemical, and mechanical signals in constant conversation.

Yet almost every medical test is scalar. That means it tries to compress all that complexity into a single number: ALT/AST, Glucose, sodium, calcium, etc.

But these aren’t states. They’re snapshots of fluctuating processes, taken out of time, out of context, and compared to statistical averages. It’s like trying to diagnose a jazz symphony using a still photo of one note.

Bodies compensate. They hide dysfunction better than machines because they adapt across systems. A cell doesn't simply "fail", it adjusts, reroutes, conserves. That’s how people with deep fatigue, autoimmune symptoms, or hormone imbalance can show “normal” labs.

Someone with thyroid hormone resistance may show normal hormone levels, but still feel exhausted, cold, or depressed. But just because someones tests are "normal", it doesn't mean their symptoms aren't real. It’s a mismatch between what the test is measuring and what the body is actually doing. Health is not determined by tests, its determined by how you experience equilibrium in your body.

Function in living systems is about rhythm, timing, feedback, not just quantity.

The same blood calcium value in one person, it might reflect bone loss and in another might be a result of soft tissue calcification. The number is the same, but the system dynamics are totally different. The scalar value flattens what should be interpreted as a pattern across time.

What keeps you alive isn't just chemistry, it’s coherence. It’s whether your systems are communicating well, adapting smoothly, and holding a resonant shape. And the tools we use to assess this? They're mostly blind to it.

Scalar testing collapses health into one-dimensional values. These numbers might suggest stability while the system is quietly falling apart. It creates the illusion of clarity, but only because it's not asking the right questions.

We will never understand life by measuring it like it's inert. Health isn’t the absence of symptoms. It’s the presence of signal integrity, and until medicine starts listening to the field instead of flattening it, we’ll keep mistaking dysfunction for normal.