Differential Diagnosis (It’s Never Lupus)

The diagnostic process is an essential component of healthcare, as an accurate and efficient diagnosis enables optimal patient outcomes (Balogh et al. 2015). This involves inspecting patient history, laboratory data, or physical examinations. However, because illnesses can have various or unpredictable symptoms, multiple possibilities must be considered before a diagnosis can be made.

Differential diagnosis (DDx) is an analysis method used to distinguish and identify a diagnosis from a list of other competing diagnoses (Cook and Décary 2020). Multiple diagnostic hypotheses are typically considered during this process, which are then narrowed to a few possibilities as the working diagnosis is refined (Balogh et al. 2015). Deductive reasoning is used to evaluate the possible clinical history and physical symptoms that may be presented if a patient has a certain disease. This is repeated for each potential condition, where the probability of each hypothesis is assessed to determine a likely diagnosis.

One reason why DDx is needed is that illnesses often present with similar symptoms, making diagnosis complex. Our bodies typically respond to infection by activating the immune system and using antibodies or white blood cells against pathogens (Drexler 2010). Common diseases can clinically present as a single syndrome but have various interacting pathways that are mechanically distinct (Reinholdt et al. 2025). Additionally, symptom similarity has been shown to relate to shared genetic associations, while the diversity of symptoms correlates with their cellular mechanism diversity (Zhou et al. 2014). On the other hand, the same illness can present differently in patients. For example, the clinical expression of illnesses such as upper respiratory tract viral infections is variable; there is a high level of human variation that influences immune response to illnesses, including comorbid conditions, patient age, physiology, immunological experience, biological sex, and the nature of the virus (Crawford 2024; Eccles 2005).

To reason diagnoses, DDx uses Bayes’ rule (Figure 1). This is a method of calculating conditional probabilities, which are probabilities that depend on the value of other probabilities (Westbury 2010). In the case of the probability of a given patient having a disease, the prevalence of the disease can be used to estimate its prior probability. This probability indicates the frequency of the disease, which can be used to determine prior evidence for or against its likelihood in the patient (Jain 2017). In its simplest form, Bayes’ rule considers two mutually exclusive possibilities, which are two conditions that cannot be true at the same time. DDx situations can be assessed where either the patient has or does not have some diagnosis (probability A) and has or does not have certain symptoms (probability B), which can then be used to determine the probability of A given B (Westbury 2010).

Clinical reasoning, which is the cognitive process used to evaluate a patient, also plays a large role in DDx. Current understanding of this uses the dual process theory, which proposes two distinct systems in which thought is processed (Balogh et al. 2015). The nonanalytical fast system 1 involves little working memory, relying on implicitly learned activities and repeated associations to make decisions. The analytical slow system 2 uses a heavy amount of working memory; it involves creating mental models to see what should or should not happen in particular scenarios, test actions, and analyze alternative causes (Balogh et al. 2015).

Medical diagnosis is rarely an immediate process. By considering multiple possibilities and narrowing them down with evidence, DDx helps physicians move from uncertainty to informed decisions about a patient’s symptoms.

References

Ananda Rao, Amogh, Milind Awale, and Sissmol Davis. 2023. “Medical Diagnosis Reimagined as a Process of Bayesian Reasoning and Elimination.” Cureus 15 (9): e45097. https://doi.org/10.7759/cureus.45097.

Balogh, Erin P., Bryan T. Miller, John R. Ball, et al. 2015. “The Diagnostic Process.” In Improving Diagnosis in Health Care. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK338593/.

Cook, Chad E., and Simon Décary. 2020. “Higher Order Thinking about Differential Diagnosis.” Brazilian Journal of Physical Therapy 24 (1): 1–7. https://doi.org/10.1016/j.bjpt.2019.01.010.

Crawford, Serena. 2024. “Why Are Some People More Susceptible to Infection?” September 30. https://medicine.yale.edu/news-article/why-are-some-people-more-susceptible-to-infection/.

Drexler, Madeline. 2010. “How Infection Works.” In What You Need to Know About Infectious Disease. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK209710/.

Eccles, Ron. 2005. “Understanding the Symptoms of the Common Cold and Influenza.” The Lancet. Infectious Diseases 5 (11): 718–25. https://doi.org/10.1016/S1473-3099(05)70270-X.

Jain, Bimal. 2017. “The Key Role of Differential Diagnosis in Diagnosis.” Diagnosis 4 (4): 239–40. https://doi.org/10.1515/dx-2017-0005.

Reinholdt, Laura, Elissa Chesler, Martin Pera, and Nadia Rosenthal. 2025. “The Rare-to-Common Disease Journey: A Winding Road to New Therapies.” Trends in Genetics 41 (9): 762–73. https://doi.org/10.1016/j.tig.2025.05.003.

Westbury, Chris F. 2010. “Bayes’ Rule for Clinicians: An Introduction.” Frontiers in Psychology 1 (November): 192. https://doi.org/10.3389/fpsyg.2010.00192.

Zhou, XueZhong, Jörg Menche, Albert-László Barabási, and Amitabh Sharma. 2014. “Human Symptoms–Disease Network.” Nature Communications 5 (1): 4212. https://doi.org/10.1038/ncomms5212.