Content updated as of October 19, 2026

Diagnosing breast cancer via infrared thermography remains a controversial practice, presenting both advantages and drawbacks. We will now examine the most up-to-date evidence.

Breast cancer is the most common cancer among women worldwide, making an accurate diagnosis crucial for successful treatment (Hashemi et al., 2019; Hand et al., 2021). Furthermore, breast cancer is the second or third most common malignant neoplasm in developing countries (Acharya et al., 2012). It is the most frequent cancer diagnosis in women under 40 years of age and the second leading cause of cancer-related death within this age demographic (Daly et al., 2021).

...the global rise in breast cancer cases makes the use of innovative interventions increasingly necessary...

Hand et al., 2021

According to the updated GLOBOCAN 2022 estimates, approximately 2.3 million new cases occurred in women, accounting for nearly 24% of all cancers diagnosed in females (Bray, F. et al., 2024).

There is a proportional relationship between breast cancer tumor growth and its temperature (Usuki H., 1990). In fact, infrared thermography was first utilized in 1956 when Lawson discovered that the skin temperature over a cancerous area in the breast was higher than that of normal tissue (Lawson R., 1956).

Due to these promising beginnings (Connell Jr et al., 1966), thermography was widely adopted as a diagnostic tool during the 1960s and 1970s. However, between the 1970s and 1990s, several studies emerged reporting a significant number of false positives (Williams et al., 1990; Moskowitz et al., 1976), calling into question the reliability of infrared thermography as a standalone diagnostic device for breast cancer. These studies drastically impacted the adoption of this technology as well as the volume of scientific publications (Figure 1) and, more importantly, inevitably damaged the reputation of infrared thermography within the medical field.

However, with the integration of automated software and machine learning techniques, thermography has evolved into an adjunctive tool to support breast cancer diagnosis. Indeed, it has been demonstrated that in clinical settings, the use of automated thermography software improves accuracy and reduces analysis time.

In recent years, advancements in camera resolution and the incorporation of machine learning algorithms have reinvigorated research in this area. Nevertheless, results remain highly dependent on the camera model, thermal protocol, patient cohort, interpretation criteria, and the gold standard reference used.

Source: Wnuk-Scardaccione A et al. 2026.

Figure 1: Number of publications on thermography up to 2020 (Adapted and updated from Sillero-Quintana et al., 2018)

“...mammography proves to be the most sensitive non-invasive technique for detecting early-stage tumors, although other radiation-free imaging screening methods exist, such as thermography, diaphanography (light scanning), whole-breast ultrasound, and magnetic resonance imaging (MRI)...”

Hashemi et al., 2019.

What current evidence says

The systematic review and meta-analysis by Goñi-Arana and colleagues, published in Systematic Reviews in 2024, included 22 clinical studies published between 2001 and 2023. The pooled sensitivity was 88.5% and the specificity was 71.8%, but heterogeneity was very high: 79.3% for sensitivity and 99.1% for specificity.

This indicates that the findings are neither directly comparable across studies nor reliably generalizable to all clinical settings. A high sensitivity in selected studies does not mean that the technique has been shown to reduce breast cancer mortality when used for population-based screening.

If thermography were to be applied to the general asymptomatic population (where the actual prevalence of breast cancer is approximately 5 cases per 1,000 women): A sensitivity of 88.5% implies that out of 5 affected women, it would detect roughly 4.

Regarding the 71.8% specificity, this means that out of 995 completely healthy women, the test would yield a false positive result in 28.2% of them.

The positive predictive value (PPV) is 1.4%, with the clinical consequence that fewer than 2 out of every 100 women who test positive on a thermogram would actually have cancer.

In summary, thermography serves as an adjunctive tool when a patient has the disease, given its high sensitivity; however, in screening contexts, it cannot replace mammography as the primary technique for breast cancer diagnosis.

The following PROS of thermography in relation to breast cancer can be highlighted:

• Higher specificity in detecting benign breast tumors compared to mammography (Hashemi et al., 2019) (Figure 2)
• Cost-effective (Omranipour et al., 2016; Kolarić et al., 2013)
• Immediate results, non-invasive, radiation-free (Hashemi et al., 2019; Singh & Singh, 2019). Updated: Utilizes no ionizing radiation and requires neither breast compression nor venous access (Goñi-Arana et al., 2024)
• Artificial intelligence using deep neural networks to analyze thermograms yields improved outcomes and represents a promising future analytical strategy (Mambou et al., 2018)
• Three-dimensional functional infrared imaging (3DIRI) operates independently of breast density and generates 3D vascular maps of the breast to detect peripheral vascular asymmetry, as well as variations in vascular morphology, density, and perfusion rate between breasts (Hellgren et al., 2019)
• Updated: Can provide functional insights into vascular and thermal patterns, complementary to the anatomical information provided by mammography or ultrasound (Goñi-Arana et al., 2024)
• Updated: Recent studies, particularly when integrated with artificial intelligence, have demonstrated high sensitivities, albeit with variable specificity (Goñi-Arana et al., 2024)
• Updated: May be relatively accessible and time-efficient in specific settings, although its actual clinical value relies on standardized protocols and adequately trained personnel (Goñi-Arana et al., 2024)

Figure 2: Statistical results of various diagnostic methods compared against biopsy as the gold standard (PPV: positive predictive value, NPV: negative predictive value). Adapted from Hashemi et al., 2019.

The CONS of infrared thermography regarding breast cancer are:

• Lack of knowledge and expertise in the application of this technique among clinicians (Hellgren et al., 2019; Hashemi et al., 2019; Ng, 2009)
• Specific environmental and patient conditions are required for the proper execution of thermography (Hashemi et al., 2019)
• Thermography demonstrates a sensitivity of 52–61%, compared to 70.6–94% for mammography (Hashemi et al., 2019; Williams et al., 1990; Omranipour et al., 2016)
• Lower specificity compared to ultrasound, yielding 78.5% and 95.3%, respectively (Alikhassi et al., 2018)
• A poor reputation within the medical field (Moskowitz et al., 1976; Fitzgerald & Berentson-Shaw, 2012)
• Has not been proven effective as a standalone screening test or for the reliable detection of early-stage breast cancer (FDA, 2023)
• Pooled specificity is moderate to low and highly variable across studies, carrying a risk of false-positive results (Goñi-Arana et al., 2024)
• Methodological heterogeneity prevents the establishment of universal values for sensitivity, specificity, or thermal cutoff points (Goñi-Arana et al., 2024)
• There is no evidence demonstrating a reduction in breast cancer mortality when utilized for screening purposes (FDA, 2023)

Despite the pros and cons of this technology, as noted by Hashemi and colleagues (2019): “...infrared thermography can also be recommended for use as a complementary tool alongside other established imaging methods for the early detection of breast cancer...” and as an adjunctive test to a clinical breast examination (Omranipour et al., 2016).

In conclusion, despite technological advancements in thermography, it cannot currently replace mammography as the primary modality for breast cancer diagnosis (Omranipour et al., 2016).

The most current evidence suggests that it may play a complementary role—particularly if protocols are standardized and the application of artificial intelligence is clinically validated—yet there remains insufficient evidence to recommend it as a standalone method for early detection.

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