A Journey Through Philosophy and Medicine: From Aristotle to Evidence-Based Decisions

Abstract

The evolution of medical reasoning is deeply intertwined with philosophical thought, beginning with Aristotle’s foundational work in deductive logic. Aristotle’s principles significantly influenced early medical practice, shaping the works of Galen and Avicenna, who made empirical observations that expanded clinical knowledge. During the Enlightenment, both inductive reasoning, as advocated by Francis Bacon, and deductive methods, as stressed by René Descartes, significantly advanced medical reasoning. These approaches proved insufficient when it came to handling uncertainty and variability in medical outcomes. Nineteenth-century figures like William Osler advanced a probabilistic understanding of medicine. Karl Popper’s 20th-century hypothetico-deductive method, which introduced the concept of falsifiability and transformed scientific inquiry into a rigorous process of hypothesis testing, is a fundamental aspect of evidence-based medicine (EBM). EBM emerged as the dominant paradigm, combining empirical research, clinical expertise, and statistical inference to guide medical decisions. Looking forward, Bayesian reasoning offers a further refinement in medical reasoning. By incorporating prior knowledge and continuously updating probabilities with new evidence, Bayesianism addresses the limitations of frequentist methods and offers a more dynamic and adaptable framework for clinical decision making. As medical reasoning evolves, understanding this philosophical lineage is essential to navigating the future of patient care, where evidence must be both rigorously tested and individually tailored.

Full text

Citation: de Alencar, J.N.; Oliveira,
M.H.d.J.; Sampaio, M.C.N.; Rego,
M.F.; Nunes, R. A Journey Through
Philosophy and Medicine: From
Aristotle to Evidence-Based Decisions.
Philosophies 2024,9, 189.
https://doi.org/10.3390/
philosophies9060189
Received: 19 November 2024
Revised: 11 December 2024
Accepted: 20 December 2024
Published: 23 December 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
A Journey Through Philosophy and Medicine: From Aristotle to
Evidence-Based Decisions
JoséNunes de Alencar 1, * , Marcio Henrique de Jesus Oliveira 2, Maria Catarina Nunes Sampaio 3, Maria
Francisca Rego 1and Rui Nunes 1
1Department of Bioethics, Universidade do Porto, 4099-002 Porto, Portugal; mfrego@med.up.pt (M.F.R.);
ruinunes@med.up.pt (R.N.)
2Department of Medicine, Universidade Federal de Uberlândia, Uberlândia 38408-100, Brazil;
marcio.ufu@outlook.com
3Department of Medicine, University Center INTA—UNINTA, Sobral 62050-100, Brazil;
catarinansampaio@hotmail.com
*Correspondence: jose.alencar@dantepazzanese.org.br
Abstract: The evolution of medical reasoning is deeply intertwined with philosophical thought,
beginning with Aristotle’s foundational work in deductive logic. Aristotle’s principles significantly
influenced early medical practice, shaping the works of Galen and Avicenna, who made empirical
observations that expanded clinical knowledge. During the Enlightenment, both inductive reasoning,
as advocated by Francis Bacon, and deductive methods, as stressed by RenéDescartes, significantly
advanced medical reasoning. These approaches proved insufficient when it came to handling uncer-
tainty and variability in medical outcomes. Nineteenth-century figures like William Osler advanced a
probabilistic understanding of medicine. Karl Popper’s 20th-century hypothetico-deductive method,
which introduced the concept of falsifiability and transformed scientific inquiry into a rigorous
process of hypothesis testing, is a fundamental aspect of evidence-based medicine (EBM). EBM
emerged as the dominant paradigm, combining empirical research, clinical expertise, and statistical
inference to guide medical decisions. Looking forward, Bayesian reasoning offers a further refinement
in medical reasoning. By incorporating prior knowledge and continuously updating probabilities
with new evidence, Bayesianism addresses the limitations of frequentist methods and offers a more
dynamic and adaptable framework for clinical decision making. As medical reasoning evolves,
understanding this philosophical lineage is essential to navigating the future of patient care, where
evidence must be both rigorously tested and individually tailored.
Keywords: scientific evolution; empirical investigation; evidence-based medicine; logical empiricism;
philosophical thought
1. Introduction
In medical reasoning, the correspondence between philosophy and science has pro-
foundly shaped modern practices. Medical methodologies have been shaped by the
ongoing pursuit of knowledge, from Aristotle’s logical and empirical contributions to the
Enlightenment’s emphasis on reason and scientific inquiry.
This article explores the philosophical evolution of medical reasoning, from Aristotle’s
innovative concepts to current applications. Through an exploration of key philosophers,
defining moments, and contemporary trends such as evidence-based medicine (EBM), we
reveal the philosophical and scientific influences shaping modern medical practices. Our
objective is to elucidate the historical and philosophical interconnections within medical
reasoning, enriching our comprehension of medical science’s development and clinical
decision-making assimilation of empirical evidence. We focus on key philosophers and
paradigms selected for their direct influence on how medical knowledge is generated,
tested, and applied, thereby connecting philosophical theories of evidence, causality, and
Philosophies 2024,9, 189. https://doi.org/10.3390/philosophies9060189 https://www.mdpi.com/journal/philosophies

Philosophies 2024,9, 189 2 of 15
scientific inference with the methods that shape diagnostic and therapeutic decisions in
clinical practice.
2. Aristotle and “Deductive Reasoning”
Aristotle (384–322 BC), one of the most influential figures in early Western philosophy,
significantly contributed to the formalization of logic. As both a student of Plato and tutor to
Alexander the Great, Aristotle broadened his understanding in metaphysics, ethics, politics,
and natural sciences. Unlike some of his contemporaries, who were often preoccupied
with abstract ideals and forms, Aristotle focused on understanding and explaining the
natural world through observation and structured logical analysis. This emphasis led to
the development of deductive reasoning. Aristotle established deductive reasoning as a
method for progressing from general principles to conclusions. For example:
Major premise: All humans are mortal.
Minor premise: Socrates is a human.
Conclusion: Therefore, Socrates is mortal.
Deductive reasoning’s power lies in its capacity to ensure a true conclusion if the
premises are veracious. Aristotle showcased this approach in various writings, including
biological classifications and ethical discussions. In his Nicomachean Ethics, he argued
that virtues are means between extremes. For instance, if all virtues are means between
extremes and courage is a virtue, then it follows that courage must be a mean between
recklessness and cowardice [
1
,
2
]. Although Aristotle’s own works were not explicitly
focused on medical applications, his systematic approach to deduction laid a conceptual
foundation. However, while Aristotelian deduction provided a logical template, medicine
soon recognized that pure deduction from general principles had limits, prompting later
thinkers to seek methods that combined empirical observation with systematic logic.
In the medical field, deductive reasoning is frequently employed in both diagnostic
and therapeutic decisions. For example, consider a diagnostic scenario: Dengue fever
causes high fever, and a patient presents with a high fever. One might deduce that this
patient has dengue fever. Despite following a logical structure, this reasoning may lead
to an inaccurate conclusion due to high fever being a symptom of numerous illnesses.
Similarly, in a therapeutic context, suppose a patient took a medication and subsequently
recovered from an illness. One might deduce that the medication cured the patient based
on the sequence of events. However, this conclusion might overlook other factors such
as the natural course of the disease or contextual effects [
3
]. Without empirical evidence
from controlled studies, attributing the recovery solely to the medication reflects a logical
misstep known as post hoc ergo propter hoc—assuming that because one event followed
another, the first caused the second. Though invaluable, deductive reasoning should be
used with care and bolstered by empirical evidence and clinical judgment [4].
3. Foundations of Medical Empiricism
Hippocrates of Kos (c. 460–c. 370 BC), often hailed as the “Father of Medicine”,
significantly advanced the practice of medicine by emphasizing empirical observation and
systematic study. Diving from the supernatural explanations of diseases, Hippocrates advo-
cated a rational approach based on careful observation of patients and their symptoms [
5
].
His works, collectively known as the Hippocratic Corpus, laid the groundwork for clinical
medicine and introduced ethical standards exemplified by the Hippocratic Oath.
Hippocrates believed that diseases were caused by environmental factors, diet, and
living habits rather than divine punishment. This perspective shifted the focus to natural
causes and encouraged physicians to carefully observe and document clinical cases. The
empirical methods promoted by Hippocrates complement Aristotle’s logical approach, as
both emphasize the importance of systematic inquiry in understanding the natural world.
Centuries later, Galen of Pergamon (c. 129–c. 216 AD) built upon Hippocratic medicine
and became one of the most influential physicians in the Roman Empire. Galen integrated
philosophical principles, particularly those of Aristotle and the Stoics, into medical prac-

Philosophies 2024,9, 189 3 of 15
tice [
6
]. He believed that understanding the body’s functions required both empirical
observation and theoretical knowledge. Due to human dissection being prohibited at
that time, Galen conducted most of his studies on animals. He has conducted research in
anatomy, physiology, pathology, and pharmacology. His theory–practice interconnection
focus aligns with Descartes’ later development of the hypothetico-deductive method. Galen
significantly impacted medical thinking throughout the Middle Ages, persisting for over a
millennium.
Abu Ali al-Husayn ibn Sina, known in the West as Avicenna (980–1037), was a Persian
polymath whose contributions significantly influenced Islamic and European medicine.
His seminal work, Al-Qanun fi al-Tibb (The Canon of Medicine), synthesized the medical
knowledge of the time, drawing upon the works of Hippocrates and Galen while incor-
porating his own observations and discoveries [
7
]. Avicenna’s Canon became a standard
medical text in European universities from the 12th to the 17th centuries. By integrat-
ing Aristotelian philosophy and medical science, he underscored the significance of both
intellectual comprehension and concrete data in illness diagnosis and cure.
By the late Middle Ages, medicine had accumulated extensive observational knowl-
edge based on the teachings of Hippocrates, Galen, and Avicenna. While this era brought
about significant medical advancements, it also highlighted the importance of authoritative
texts and the limited availability of experimental evidence. The need for a scientific method
that integrated empirical evidence with systematic investigation arose. The concept of
questioning traditional doctrines and embracing humanism marked the beginning of the
Renaissance [8].
4. The Enlightenment and the Emergence of Modern Scientific Methods
The Enlightenment of the 17th and 18th centuries marked a pivotal transformation in the
quest for knowledge, expanding upon the intellectual foundation laid during the Renaissance.
During this time, scholars increasingly aspired to identify objective truths about the natural
world through reasoned inquiry and empirical evidence, challenging previously unquestioned
authorities. Thinkers like Francis Bacon (1561–1626) and Isaac Newton were instrumental
in this intellectual movement, promoting empirical methods and inductive reasoning as
fundamental tools for understanding nature [9,10].
In his pioneering work Novum Organum (1620), Bacon critiqued the traditional
Aristotelian
deductive methods and advocated for knowledge acquisition through empirical evidence and
experimentation. He argued that science should proceed from careful observation of particu-
lars to the formulation of general laws—a process he referred to as “true induction” [8,11].
Inductive reasoning operates on the principle that general truths are inferred from
specific observations [
12
]. In medicine, this involves forming generalizations based on
individual cases or clinical experiences. For instance, a physician might observe that
several patients diagnosed with dengue fever exhibit high fever, severe headache, and
joint pain. Noticing this pattern, the physician inductively infers that when a new patient
presents with these symptoms, there is a likelihood that the patient has dengue fever. This
reasoning moves from specific instances to a broader generalization about the disease’s
symptomatology. Similarly, suppose a physician administers a particular medication to
multiple patients with arrhythmias and observes that their premature beats consistently
decrease after treatment. From these specific observations, the physician may infer that this
medication is generally effective in preventing death due to arrhythmia. This inductive
inference guides the physician to prescribe the same medication to other patients with
similar conditions.
However, induction is not limited to generalizing from individual cases. It can
also arise from systematically collected datasets, statistical syllogisms, and probabilistic
forecasts—ranging from broad generalizations about disease patterns to specific predictions
about individual patient outcomes. In medicine, clinicians often draw on multiple forms
of inductive inference, whether extrapolating from well-established statistical evidence or
transferring insights from one patient’s experience to another’s comparable condition.

Philosophies 2024,9, 189 4 of 15
Inductive reasoning is susceptible to limitations from factors like small sample sizes,
selection biases, and unrecognized variables. Though inductive reasoning can generate
hypotheses and guide clinical decisions, it requires careful application and empirical
validation. Bacon’s emphasis on empirical induction laid a foundation for knowledge
construction based on careful observation and systematic data gathering. This focus
directly addressed how to move beyond mere anecdotal observations toward more stable,
evidence-informed conclusions. Concurrently, the era sought a counterpart approach: one
that could integrate mathematical rigor and logical structure to refine scientific inquiry
further. It is in this context that we turn to Descartes, whose contributions complemented
Bacon’s by offering a deductive, principle-driven framework for verifying and organizing
medical knowledge.
RenéDescartes (1596–1650), often called the “father of modern philosophy”, took a different
approach to scientific inquiry. Descartes aimed to establish a new foundation for knowledge
based on reason and clear, self-evident principles [
13
]. Descartes believed that observation alone
was insufficient for acquiring genuine wisdom. He proposed applying deductive reasoning,
where theories are verified through empirical evidence. The Discourse on Method introduced a
scientific investigation approach grounded in logic and theory, not just empiricism [14,15].
Descartes’ influence deeply affected various disciplines including mathematics, physics,
and psychology, significantly transforming the sciences, particularly medicine. The use
of logical deduction to validate empirical observations set the stage for ongoing dialogue
between theory and practice in scientific developments. Reason’s essential role in science
lies in its ability to correct the misleading information provided by the senses. This method
was not just theoretical; it provided science with a strong base for deriving universal
truths [
16
]. In medicine, deductive reasoning might involve starting with established
physiological principles and logically deriving conclusions about specific cases. For ex-
ample, suppose a physician suspects a patient has dengue fever based on the presence of
high fever, joint pain, and other characteristic symptoms. Rather than relying solely on
observation, this physician might begin recording notes on similar patients and comparing
their outcomes. By observing these records, the physician’s deduction—starting with the
general principle that dengue causes fever and pain—moves toward a more methodical
conclusion. Descartes also applied his rationalist framework to understand human physiol-
ogy in mechanistic terms, proposing that bodily functions could be explained by principles
akin to those governing physical systems [
17
]. By interpreting bodily functions through a
mechanistic lens, Descartes provided an early conceptual model for integrating rational
principles directly into the study of anatomy and physiology, thus indirectly influencing
how medical knowledge would be structured and tested.
While both Bacon and Descartes sought to reform scientific methodology, they di-
verged in their approaches. Bacon emphasized inductive reasoning from empirical data
to general principles, aiming to build knowledge from the ground up. Descartes, con-
versely, emphasized deductive reasoning from foundational truths, building knowledge in
a top-down manner.
Despite the widespread adoption of these methods, questions arose about their foun-
dational validity. Overlooking individual complexities when making such inferences can
result in inaccuracies. This problem arises from the numerous variables—including genetics,
environment, and lifestyle—that influence treatment efficacy. The method’s reliability hinges
on the assumption that the observed cases accurately represent the larger population.
At this point, the central philosophical questions confronting medical reasoning in-
volved how to justify causal inferences and ensure reliable generalizations from observed
cases. As medical inquiry became more sophisticated, understanding the nature of uncer-
tainty and the conditions under which inferences could be deemed credible emerged as a
key epistemological challenge. This set the stage for critical examinations by philosophers
like David Hume and, subsequently, Immanuel Kant, whose work continues to inform
current debates on what constitutes valid evidence in both scientific and clinical domains.

Philosophies 2024,9, 189 5 of 15
The skepticism paved the way for philosophers like David Hume (1711–1776) to
scrutinize the foundations of empirical methods. Hume’s investigations cast doubt on the
certainty of inductive reasoning, leading to a reassessment of the foundations of knowledge
validation. In An Enquiry Concerning Human Understanding (1748), according to Hume,
inductive reasoning relies on the unfounded assumption that future occurrences will resem-
ble past ones [
18
]. In medical research, the issue of induction underscores the fundamental
uncertainty of predicting outcomes based on past observations. A medication’s past ef-
fectiveness may not guarantee the same outcome for a new patient. Hume’s skepticism
emphasizes that inductive conclusions are not necessarily certain, as they rely on the
uniformity of nature—a principle that cannot be proven.
Seeking to resolve the dilemma posed by Hume’s skepticism, Immanuel Kant
(1724–1804)
embarked on a critical project to reconcile rationalism and empiricism. Kant aimed to es-
tablish a secure foundation for human knowledge that could withstand such critiques. In
the
Critique of Pure Reason (1781)
, Kant contended that although knowledge originates from
experience, not all of it is derived solely from experience [
19
]. He introduced the idea of
innate structures and categories influencing knowledge acquisition as synthetic a priori knowl-
edge. This blend of rationalism and empiricism aimed to address the doubts raised by Hume.
According to Kant,
the human mind plays a pivotal role in interpreting and organizing empiri-
cal data. Clinicians in medicine rely not only on empirical evidence but also employ inherent
reasoning structures to interpret intricate patient data. This approach acknowledges the lim-
itations of pure empiricism and highlights the necessity of integrating rational principles to
enhance medical decision making.
5. Application of Scientific Methods in 19th-Century Medicine
Building upon the scientific methodologies developed during the Enlightenment, the
19th century witnessed significant advancements in medical practice. The integration of
empirical observations with theoretical frameworks led to transformative discoveries and
practices in medicine. Physicians and researchers began to apply the principles of inductive
and deductive reasoning, as established by thinkers like Francis Bacon and RenéDescartes,
to address complex medical challenges.
Ignaz Semmelweis (1818–1865), a Hungarian physician and notable figure in medical
history, made revolutionary discoveries about the importance of hand hygiene in 1847.
Despite the absence of a clear theoretical explanation, his practice significantly lowered
death rates during childbirth. Semmelweis’s findings, despite strong empirical evidence,
lacked widespread acceptance due to the absence of a theoretical framework [20].
Florence Nightingale (1820–1910), a British social reformer, statistician, and the founder
of modern nursing, successfully applied principles similar to the deductive method in
her nursing practice. During the Crimean War, Nightingale analyzed statistical data to
anticipate and deter patient mortality trends. For her time, her patient care decisions
were informed by logical analysis, making her evidence-based approach innovative [
21
].
Nightingale’s evidence-based approach integrated empirical observations with logical
analysis, embodying the synthesis of inductive and deductive methods advocated by
Enlightenment thinkers [22–25].
Claude Bernard (1813–1878), a 19th-century French physician and physiologist, re-
inforced the argument that scientific knowledge is best obtained through controlled ex-
perimentation and deduction. In the lab, medicine could be turned into a science due to
the controlled testing of hypotheses contrasting the unpredictability of clinical settings.
Bernard considered clinical practice as essential but insufficient for producing scientific
knowledge [
26
–
28
]. Bernard’s position emphasized that science requires both theory and
experimentation, underscoring the dual emphasis on deduction and empirical validation.
His main work, An Introduction to the Study of Experimental Medicine (1865), outlined this
approach, defending the hypothetico-deductive method in medical experimentation [
26
,
29
].
He believed that medicine could be transformed into a true science through laboratory

Philosophies 2024,9, 189 6 of 15
experimentation, where variables could be controlled, contrasting with the unpredictability
of clinical settings.
These advancements were not without their critics. Thinkers like Risueño d’Amador
questioned the increasing reliance on statistical methods in medicine, arguing that such
approaches prioritized averages and generalizations over personalized patient care [
30
].
He contended that medical practice should adapt to individual patients through intuition
and experience rather than being confined to rigid statistical models. This critique reflects
the ongoing tension between empiricism and rationalism in medicine—a dilemma that has
persisted since the debates of the Enlightenment.
6. The Positivism of the 20th Century
Despite the significant advancements in medical science during the 19th century,
many of the existing methods of scientific inquiry, particularly those rooted in inductive
reasoning, faced growing criticism. These limitations underscored the need for a more
rigorous and systematic approach to scientific investigation. Figures like William Osler
(1849–1919), the father of modern internal medicine, were among those who recognized
the flaws in earlier logical constructions [
31
]. Osler often pointed out the insufficiencies
in medical practice where conclusions drawn from individual observations could lead to
faulty clinical reasoning. Osler stated that medicine is “a science of uncertainty and an
art of probability” [
32
]. Thus, simple observations or even rudimentary notes made up to
that point were insufficient to determine a diagnosis or the success of a therapy. A new
revolution was necessary—one capable of recognizing the probabilistic nuances inherent in
medicine, allowing for a more structured approach to uncertainty.
Auguste Comte (1798–1857), the father of positivism, advocated for scientific knowl-
edge advancement based on observable phenomena and rational inference, dismissing
metaphysical interpretations [
33
]. Positivism emerged as a philosophical perspective
holding that a single, objective reality can be identified, quantified, and comprehensively
understood through empirical observation and logical analysis. This perspective allows for
prediction and explanation within a causal framework. Causal inferences in this paradigm
are based on three primary criteria: association (a reasonable link between cause and effect),
temporal precedence (the cause occurs before the effect), and the absence of confound-
ing variables (ensuring no other factors influence the outcome). According to positivists,
knowledge must be generated objectively, devoid of biases or preconceptions held by the
researcher or participants. They argue that truth is represented by knowledge that has been
rigorously developed; it is certain, consistent with reality, and precise [34].
To ensure the development of this “truth”, an absolute separation between the research
participant and researcher is necessary. To maintain this distinction, positivists employ
a strategy of dualism and objectivity, arguing that it is, in fact, feasible to differentiate
between the observer and the object of observation [35].
Logical positivism, which originated in the 1920s, is a branch of positivism that confers
significance solely on propositions that can be empirically verified. This school of thought
emerged from the Vienna Circle, a group of prominent philosophers and scientists based in
Vienna, including Moritz Schlick, Rudolf Carnap, and Otto Neurath. The Circle was deeply
involved in the philosophical aspects of formal and physical sciences. Their preoccupation
with the logical examination of scientific methodologies and their implications substantially
affected the trajectory and nature of their philosophical investigation [36].
Central to the Vienna Circle’s philosophy was its stance on science and its relationship
to other domains of thought. They rejected the possibility of validating knowledge claims
that extended beyond the empirical scope of science. Consequently, they dismissed vast
swaths of metaphysics, along with many theological and ethical claims, as nonsensical or
unverifiable. This dismissal was rooted in their fundamental criterion for meaningfulness:
a statement or proposition could only be considered meaningful if it could, in principle, be
empirically verified [37].

Philosophies 2024,9, 189 7 of 15
The Vienna Circle and its logical positivism exerted a significant impact that tran-
scended their origins in Vienna, especially due to the migration of its members to Britain
and the United States during the rise of Nazism. The influence of logical positivism on
Anglo-American analytic philosophy was profound. The principles established by the
Vienna Circle—rigorous empirical scrutiny and a dedication to a scientific approach to
philosophical inquiry—have had a lasting impact on the philosophical landscape and con-
tinue to shape modern thought in numerous disciplines, including methodologies that form
the foundation of evidence-based medicine. The influence of positivist thinking remains
pervasive in modern clinical and basic science research. International scientific standards
published in reputable journals and professional organizations confirm these. As a result,
positivist thought has a substantial impact on scientific progress and the methodologies
employed by clinicians in their pursuit of knowledge [38].
Post-positivism emerged during the mid-20th century as an extension and critical
reaction to the fundamental tenets of positivism. This paradigm recognizes the limitations
and criticisms of positivism, particularly regarding the generalization of findings from
specific observations. Prominent figures such as Karl Popper were instrumental in the
opposition to positivism, specifically its inability to distinguish between scientific and
pseudoscientific theories, despite both appearing capable of accumulating supporting evi-
dence. The hypothetico-deductive method, most systematically articulated by
Karl Popper,
marked a crucial advancement in the evolution of scientific reasoning. While deductive
logic had its roots in Aristotle, Popper redefined it by asserting that scientific theories should
not merely be tested for verification but also be subject to falsification.
Popper argued
that
true scientific progress occurs not when a hypothesis is confirmed but when it is rigorously
tested and stands up to attempts to disprove it. For Popper, the hallmark of a good theory
was its ability to generate predictions that could be empirically tested and potentially
refuted, allowing science to evolve through a process of elimination. For Popper, falsifica-
tion was not just an additional step but the most valuable approach to demarcate science
from pseudoscience. Unlike scientific theories that remain open to being proven wrong,
pseudoscientific claims are often structured so they cannot be falsified—or, if evidence
accumulates against them, their proponents do not abandon them [39].
The “black swan” example illustrates Popper’s critique: observing only white swans
could lead to the erroneous conclusion that all swans are white. This conclusion would be
disproven by the discovery of a single black swan, demonstrating Popper’s assertion that
scientific advancement occurs not through the verification of hypotheses but through their
falsification. This methodology represents a substantial shift from the positivist framework,
which tends to regard knowledge as more immutable.
Post-positivism presents a nuanced view that the veracity of theories can never be
conclusively established. Similar to positivism, it upholds the use of observation and
measurement but acknowledges the intrinsic limitations associated with these approaches.
For post-positivists, the pursuit of scientific knowledge is slow, progressive, and iterative,
characterized by attempts to refine theories by demonstrating their incompleteness or
incorrectness. This paradigm is characterized by scientific humility that acknowledges the
certainty of truth’s existence and the probabilistic approximation of our understanding of
it, but complete comprehension will forever elude us [11].
7. The Most Recent Paradigm Shift in Medical Reasoning: Evidence-Based Medicine
Evidence-based medicine (EBM) emerges as a culmination of centuries of intellectual
exploration into the nature of knowledge, evidence, and scientific inquiry. EBM represents a
synthesis of the methodologies and principles advocated by philosophers such as Aristotle,
Francis Bacon, David Hume, the positivists, and, maybe more importantly, Karl Popper.
The concept of “normal science” created by Thomas Kuhn (1922–1996) further clarifies
the development of scientific paradigms [
40
]. According to Kuhn, normal science operates
within the confines of a shared paradigm encompassing the collective beliefs, values, and
techniques of the scientific community. Within this context, scientists focus on solving

Philosophies 2024,9, 189 8 of 15
residual puzzles, rarely seeking major novelties. Kuhn posits that while periods of normal
science are prevalent, occasionally a crisis triggers a “scientific revolution”, overthrowing
the old paradigm and instituting a new one.
EBM emerges as a new paradigm that shifts from a purely rationalist or empiricist
approach to one that incorporates insights from post-positivism [
41
]. It recognizes the
probabilistic character of medical knowledge and practice and stresses the importance of
basing medical decisions on the strongest evidence currently available, frequently derived
from rigorously conducted research and statistical data. EBM questions the conventional
dependence on expert opinion and pathophysiological reasoning in isolation. It promotes
a systematic and empirical approach to understanding and managing health conditions.
Medical practitioners must critically evaluate the evidence, understand its limitations, and
tailor its application to each patient’s unique circumstances and values [42].
EBM is currently defined as “the conscientious, explicit, and judicious use of current best
evidence in making decisions about the care of individual patients or the delivery of health
services” [
43
,
44
]. This definition emphasizes the purposeful and meticulous incorporation of
the most reliable research evidence into the process of making clinical decisions [45].
Furthermore, in the context of EBM, the importance of applying the hypothetico-
deductive method becomes evident when addressing diagnostic and therapeutic challenges.
For example, in diagnosing dengue fever, the presence of symptoms like fever, body pain,
and joint pain are not absolute indicators of the disease but are rather probabilistic in nature.
Given this, medical reasoning must be grounded in rigorous studies that test hypotheses
methodically. This involves comparing the presence of these symptoms in “cases” (patients
diagnosed with dengue) and “controls” (patients without dengue but possibly suffering
from similar diseases). By employing a case–control model, physicians can calculate the
probability of a patient having dengue versus another illness, based on the presence of
these symptoms.
The evaluation of therapies must also consider the probabilistic nature of clinical
outcomes. If the improvement or deterioration of a symptom, or the progression toward
recovery or death, is probabilistic, merely observing that an individual took a medication
and recovered, for example, does not provide absolute proof of the treatment’s efficacy.
For instance, in a disease with a 10% mortality rate, even after administering an inert
treatment, there is still a 90% chance of survival. How, then, can we ascertain whether
a treatment is effective or inert? The hypothetico-deductive approach addresses this by
employing randomized controlled trials (RCTs), where patients are randomly assigned to
two groups—one receiving the treatment and the other receiving a placebo or standard care.
Randomization helps ensure that both groups are comparable, with the primary difference
being the administration of the treatment [46].
If, at the end of the study, only 6% of patients in the treatment group die, this suggests
that the treatment has reduced mortality from 10% to 6%, thus demonstrating its efficacy.
However, from the perspective of the family of a patient who dies despite receiving the
treatment, the drug may seem ineffective. Without comparing outcomes between the
treated and control groups, individual cases of survival or death offer limited insight. It is
through this comparison, based on the probabilistic model, that we can distinguish effective
treatments from those that are merely coincidental. The rigorous testing of hypotheses
through the hypothetico-deductive framework is therefore essential in avoiding misleading
conclusions based on isolated observations, and it serves as the foundation upon which
EBM is built today.
For an RCT to be more credible, it must have sufficient “statistical power”. Statistical
power (1
−β
) denotes the probability that a study will detect a genuine effect, assuming
one truly exists. Determining power involves specifying the expected magnitude of the
intervention’s effect, the variance in patient responses, the chosen significance level (
α
), and
the intended sample size. By carefully planning these parameters during the study design
phase, researchers aim to minimize the risk of incorrectly concluding that no meaningful
benefit exists (a Type II error). An adequately powered RCT is thus generally seen as more

Philosophies 2024,9, 189 9 of 15
credible within clinical circles, as it reduces the likelihood of overlooking a substantive
therapeutic advantage [47].
While an adequately powered RCT is often deemed more reliable, some scholars
argue that the focus on strict statistical thresholds can obscure nuanced clinical realities.
According to Worrall, the insistence on randomization does not necessarily provide a robust
epistemic advantage, as the actual balancing of unknown confounders remains a matter
of luck in any single trial [
48
]. Cartwright similarly contends that while the notion of an
“ideal” randomized experiment might justify causal claims in theory, real-world trials never
fully meet these idealized conditions, thus challenging the assumption that randomization
alone confers superior evidential weight [
49
]. Consequently, while RCTs remain a valuable
tool, their elevated status as the “gold standard” of evidence should be tempered with an
awareness of their philosophical limitations and the nuanced clinical realities that their
simplistic statistical thresholds can obscure.
Beyond individual trials, research synthesis techniques such as meta-analyses have
risen to prominence in EBM, aiming to consolidate evidence across multiple RCTs and other
studies [
50
]. By integrating diverse sources of evidence and applying rigorous inclusion
criteria, meta-analyses aim to offer more comprehensive estimates of treatment efficacy or
diagnostic accuracy. However, philosophers have highlighted challenges such as combining
heterogeneous studies and potential biases in study selection, casting doubt on whether
meta-analysis can truly serve as a “platinum standard” [
51
]. High-quality meta-analyses
employ random-effects models and transparent methodologies to minimize these concerns,
reinforcing their role as valuable complements to RCTs.
There are many other substantial philosophical and practical criticisms of EBM. Draw-
ing on Thomas Kuhn’s perspective, some argue that positioning EBM as an independent
paradigm within medicine risks stripping medicine of its broader epistemological signifi-
cance if it is detached from the encompassing discipline’s theoretical structure. Without
firmly situating EBM within the wider clinical and scientific context, practitioners may
be inundated with a profusion of increasingly complex data lacking coherent meaning,
potentially legitimizing a new form of technocratic authority rather than empowering
informed clinical judgment [
52
]. Similarly, critics note a paradox: while proponents of EBM
may challenge current medical practice systems for relying on unsystematic observations
and personal convictions, they often fail to produce robust evidence supporting EBM’s own
necessity and superiority [
52
]. Moreover, EBM’s early stance against medical authority—
eschewing the “opinion of local or international experts” in favor of independent physician
assessment—has evolved into a reliance on protocols and summaries produced by a select
cohort of experts. This reversal suggests a concession to authority, so long as it aligns with
EBM’s framework, raising concerns about internal inconsistencies [53].
The very term “evidence-based medicine” has been criticized as potentially mislead-
ing [
54
]. It may imply a false dichotomy between “evidence-based” and “non-evidence-based”
medicine, hinting that only EBM practitioners grasp scientific truth. Critics highlight that
not all clinically relevant questions are strictly scientific; medicine stands at the crossroads of
biology, sociology, and psychology. Clinical decisions are influenced by patient individuality
and contextual complexities. Additionally, there is concern that EBM’s focus on statistical
outcomes and standardized protocols could erode medicine’s artistic dimension and person-
centered ethos, reducing patient care to quantitative measures [
55
,
56
]. Although EBM has
sometimes been associated with Karl Popper’s hypothetico-deductive model [
57
], this cor-
relation is not as straightforward as it may appear. Popper’s philosophy demands constant
attempts at falsification and thrives on robust challenges to any given hypothesis—traits that
are not fully mirrored in EBM. Although formulating testable clinical questions and seeking
evidence aligns superficially with Popper’s insistence on falsifiability, EBM’s reliance on
standardized procedures, established hierarchies of evidence, and adherence to set protocols
contrasts sharply with Popper’s emphasis on perpetual critical scrutiny and the absence of
any permanent methodological safety nets [
58
]. Finally, much of the philosophical litera-

Philosophies 2024,9, 189 10 of 15
ture on medicine critiques EBM for its positivist tendencies, particularly its prioritization of
standardized evidence hierarchies over individualized contexts.
Objections to EBM and RCTs raise important philosophical questions, but none con-
vincingly displace the randomized trial as the most reliable tool for testing treatment
efficacy in practical contexts. The fact that RCTs are not flawless—that they may fail to
achieve a perfectly balanced distribution of all known and unknown confounders—does
not undermine their substantial epistemic value. Instead, these limitations highlight the
importance of understanding the realistic goals of “gold standard” methodologies. RCTs
are not expected to provide deductive certainty about causal links; rather, their strength
lies in reducing the likelihood of systematic bias through randomization, blinding, and
rigorous statistical power. An RCT need not secure an impossible deductive certainty about
causal links. In the context of medical research, where underlying biological mechanisms
are often better understood and where plausible confounders can usually be identified
and controlled, RCTs stand out as particularly well suited for their task. They may not
guarantee universal causal truths, but they do offer a comparatively stable and rigorous
basis for informed clinical decision making [59].
8. The Next Revolution in Medical Reasoning: Bayesian Reasoning
Bayesian reasoning represents a significant paradigm shift in statistical inference
and medical reasoning, offering a dynamic framework for updating beliefs based on new
evidence. This approach addresses some of the limitations inherent in traditional frequentist
methods, which have dominated medical research and practice.
Thomas Bayes (1701–1761), an English mathematician and Presbyterian minister, laid
the foundation for this revolution. In his posthumously published work, “An Essay towards
Solving a Problem in the Doctrine of Chances” (1763), Bayes introduced what is now known
as Bayes’ theorem [
60
]. This theorem provides a mathematical formula for updating
probabilities considering new evidence, allowing for a more flexible and iterative approach
to statistical inference.
The positivist or post-positivist foundation of causal inference in modern healthcare is
supported by this deductive-hypothetic reasoning. Yet, relying exclusively on this approach
earns it the designation of “frequentist”. Scientific inference is essentially pursued in
classical (frequentist) research through the design of studies to collect data and generate
corresponding probabilities (p-values). The p-values indicate the conditional probabilities
of observing the collected data, or data that are more extreme, under the assumption
that the null hypothesis (H0) is valid [
61
]. Following this, the p-values are compared to
a pre-established criterion (level of significance), which functions as a decision-making
instrument regarding the hypotheses being evaluated. Standard procedure dictates that
the null hypothesis (H0) is rejected when pis less than 0.05 and not rejected when the
p-value surpasses the predetermined threshold (p> 0.05). The process referred to as “null
hypothesis significance testing” (NHST) is influenced by p-values [62].
It is naive to represent this as a complete and accurate scientific methodology, as it
fails to acknowledge a crucial factor: the researcher’s subjective intentions, which influence
frequentist inference. For example, the researcher’s sampling intentions have a significant
impact on the final p-value. Diverse intentions with respect to the timing of data collection
cessation may yield unique p-values for identical datasets. In addition to the researcher’s
overt intentions, p-values are also dependent on unobserved data and decisions that were
not carried out. This statement suggests that p-values may be affected by data that were
never observed (the hypothetical sampling distribution). It is contended that this violates
the conditionality principle, which states that statistical inferences ought to be grounded ex-
clusively on the data that were actually observed [
63
]. One of the most concerning features
of NHST is its inherent propensity to consistently produce a statistically significant result
(e.g., 0.05 or 0.01) through the iterative recalculation of p-values as new data are collected
and cessation of the process when the value falls below the predetermined significance
level [
64
]. This outcome is inevitable in frequentist statistics over time, notwithstanding

Philosophies 2024,9, 189 11 of 15
the validity of the null hypothesis. In summary, the frequentists’ deductive–hypothetical
model is vulnerable to manipulations, which increases the likelihood of fraudulent inter-
pretations [65–67].
Bayesian reasoning addresses these issues by incorporating prior knowledge or beliefs
(priors) and updating them with new data to obtain posterior probabilities. According to
Bayesian philosophy, probabilities are subjective degrees of belief that are updated as new
evidence becomes available [
68
]. This is mathematically expressed through Bayes’ theorem:
P(H|E)=P(E|H)·P(H)
P(E)
where:
•P(H|E) is the posterior probability of the hypothesis H given the evidence E;
•P(E|H) is the likelihood of observing the evidence E if the hypothesis H is true;
•P(H) is the prior probability of the hypothesis H;
•P(E) is the probability of the evidence E under all hypotheses.
According to the Bayesian philosophy of science, probabilities should be assigned to
propositions (e.g., theories or parameter estimates) in accordance with the degree of belief
associated with said propositions. The probabilities should subsequently be revised in
accordance with Bayes’ theorem to account for new evidence [
69
]. A reasoner confronted
with evidence (E) in Bayesian epistemology should modify their conviction in a hypothesis
(H) in accordance with the probability of the hypothesis given the evidence, denoted as
P(H|E), in comparison to their initial conviction in the hypothesis, denoted as P(H). This
updating should be conducted in accordance with Bayes’ theorem [
70
]. Bayes’ theorem
serves as the foundation for numerous tools that enable normative statements regarding the
appropriate response to new evidence, under the assumption that priors are specific [71].
By adopting this perspective, it is possible to deduce that an overwhelming majority of
research discoveries are unfounded. Doctors are more equipped to effectively refute these
and other legitimate criticisms leveled against the research establishment by integrating
a Bayesian framework into their scientific study analyses. To provide an illustration, the
analysis begins with an evaluation of the pre-test probability of a given study, denoted as
P(H), which incorporates additional relevant evidence and pre-clinical studies [72].
One notable application of Bayesian reasoning in medical research is the critique of
the reliability of published findings. John P. A. Ioannidis, in his influential paper “Why
Most Published Research Findings Are False”, used Bayesian principles to argue that the
probability of research findings being true is often low due to biases, small sample sizes,
and other factors. Ioannidis highlighted that without considering the prior probability
of hypotheses and the totality of evidence, the frequentist approach might overstate the
significance of findings [73].
A clinical hypothesis is considered to have a high pre-test probability when it is
substantiated by pre-clinical research of superior quality. A well-executed, unbiased
RCT would then have confirmatory power. Conversely, a hypothesis that arises from
dubious sources or fraudulent undertakings has an extremely low likelihood of transpiring
fortuitously as true. Thus, even for a methodologically sound RCT, the pre-test probability
of such a hypothesis prior to the study is low, and physicians should not be satisfied with
the results. A more rigorous and skepticism-based approach is required in this instance,
as confirmation and replication from many independent studies are necessary. Table 1
provides an illustration of the effects.

Philosophies 2024,9, 189 12 of 15
Table 1. Post-test probabilities, estimated powers, biases, and pre-test probabilities for various
combinations of studies. Since the result was positive, the post-test probability value indicates the
likelihood that it was a true positive [73].
Power (1 −β)Pre-Test
Probability Bias Practical Example Post-Test
Probability
80% 50% 10%
Randomized clinical trial with adequate power, low incidence
of bias, and a pre-test probability of 50%. 85%
95% 66% 30% Confirmatory meta-analysis using high-quality studies of a
good hypothesis (pre-test probability of 66%). 85%
80% 25% 40% Meta-analysis of small inconclusive studies of a moderate
hypothesis (25% pre-test probability). 41%
20% 16% 20% Phase I or II clinical trial with low power but good
methodology for a rising hypothesis (16% pre-test probability).
23%
Yet, Bayesian reasoning is not devoid of critique. Its reliance on prior probabilities
can introduce subjective elements, and disagreements persist regarding which priors are
appropriate. Furthermore, Bayesian analyses require careful interpretation, as the probability
revisions depend heavily on the quality and representativeness of both prior information and
incoming data [
74
]. In this sense, Bayesianism, like previous paradigms, offers a valuable
framework but does not eliminate philosophical and methodological controversies.
9. Conclusions
In this article, we have explored the intricate relationship between philosophy and
medical reasoning, tracing the historical evolution from Aristotle’s deductive logic to the
modern applications of Bayesian reasoning in clinical practice. We examined how key
philosophical paradigms, including empiricism, positivism, and post-positivism, have
shaped the foundations of medical science. By integrating these philosophical insights with
the development of evidence-based medicine (EBM), we illustrated how medical decision
making has progressively shifted from relying on expert opinion and theoretical models to
a more nuanced, data-driven approach that accommodates both empirical evidence and
individual patient variability.
Medical reasoning is not static; it evolves alongside advances in both philosophy
and science. As we continue to confront the limitations of traditional methods, Bayesian
reasoning offers a promising framework for updating clinical knowledge in real time based
on new evidence. This dynamic approach not only strengthens the accuracy of medical
decisions but also addresses some of the inherent biases of frequentist methods. For
clinicians, understanding these shifts is critical, as it empowers them to practice medicine
that is not only evidence-based but also philosophically grounded in a more flexible and
probabilistic understanding of knowledge.
Author Contributions: Conceptualization, J.N.d.A.; methodology, J.N.d.A.; investigation, J.N.d.A.,
M.H.d.J.O. and M.C.N.S.; writing—original draft preparation, J.N.d.A., M.H.d.J.O. and M.C.N.S.;
writing—review and editing, J.N.d.A., M.F.R. and R.N.; supervision, J.N.d.A.; project administration,
J.N.d.A. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The original contributions presented in this study are included in the
article. Further inquiries can be directed to the corresponding authors.
Conflicts of Interest: The authors declare no conflict of interest.

Philosophies 2024,9, 189 13 of 15
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