How AI Is Shaping Pain Clinical Trials and Managing Placebo Response Rates

How AI Is Shaping Pain Clinical Trials and Managing Placebo Response Rates

Chronic pain clinical trials continue to face persistent challenges around data quality, subjectivity, and high placebo response rates. An estimated 20.9% of US adults were living with chronic pain in 2021, with 6.9% experiencing high impact chronic pain that limited daily activities¹. Despite this unmet need, pain clinical trials have some of the lowest progression rates across therapeutic areas².

Challenges of data collection in pain trials

One contributing factor is the difficulty of measuring pain itself, which is inherently subjective and influenced by psychological, environmental, and contextual factors.

Standardized outcome measures such as the Visual Analog Scale and Numeric Rating Scale have been introduced, but the challenge is that pain is highly subjective and strongly influenced by psychological factors. Patient-specific life events, side effects, and expectations can influence symptom reporting on any given day, making these measures difficult to predict or control.

Placebo response presents a further challenge. In pain clinical trials, placebo response may account for as much as 75% of the observed analgesic effect³, obscuring true treatment signals and increasing the risk of failed studies.

According to Marco Calabresi , M.D, PhD, Senior Medical Director at Fortrea, the industry has implemented several strategies to combat these effects, but methods have often fallen short. “Protocol design elements such as placebo run-in periods and efforts to recruit ‘non-placebo responders’ are costly, wasteful, and have not been shown to be effective in general,” Dr. Calabresi explains.

Improving data quality with ecological momentary assessment (EMA) and AI

Ecological momentary assessment (EMA) is increasingly recognized as best practice for capturing patient reported outcomes in pain clinical trials. By collecting data in real time and within a patient’s natural environment, EMA helps reduce recall bias and provides a more accurate representation of symptom variability⁴. This approach is particularly relevant in chronic pain research, where day-to-day fluctuations are common and difficult to reconstruct during clinic visits

When delivered through electronic devices, EMA supports centralized oversight and enables continuous clinical trial monitoring. It also opens opportunities to integrate wearable data and other digital health signals, providing a richer, more contextualized dataset. However, the volume and frequency of EMA data can quickly exceed the capacity of manual review, creating new operational challenges.

AI in neuroscience is increasingly being applied to address these data challenges. In pain clinical trials, AI can support the analysis of high frequency EMA data by identifying patterns, trends, and anomalies in near real time. This allows study teams to detect data quality issues earlier and focus human review where it is most needed.

Adoption of digital tools in pain trials is rising. In 2024, 8.2% of new pain trials in Global Data's Clinical Trials database incorporated mobile or remote monitoring technologies, nearly doubling their use compared with 2018⁵. As this trend continues, combining digital health innovation with AI driven analytics offers a scalable way to manage complex datasets while maintaining blinded oversight.

Addressing placebo response with responsible AI use

Beyond monitoring, AI may contribute to a more nuanced understanding of placebo response rates. There are several ways in which a patient’s propensity to induce a placebo response can be predicted using both psychometric data – personality traits that correlate with placebo response – and biomarkers such as brain activity and genetics. This has paved the way for AI models to harness such data during screening and enrollment of pain trials, however, Dr. Calabresi warns that there’s a right and a wrong way to use such models in pain research, and that the end goal should always be to enhance the reliability and interpretability of clinical trial outcomes. As one example, AI models can be used within “propensity weighting”, helping to randomize patients in a way that support a balance of placebo responders across each arm⁶.

“Rather than selecting for a specific type of reporter in a way that could bias results, a better approach focuses on identifying patterns of consistent and meaningful reporting,” Dr. Calabresi explains. “This enables real-time enrichment of study populations by leveraging historical and behavioral data to account for placebo susceptibility, not to exclude or skew, but to support the understanding and transparent incorporation of variability into analysis. The ultimate aim is to improve signal detection while preserving the integrity and representativeness of the data.”

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References

1. Rickard S, Strahan A, et al. Chronic pain among adults—United States, 2019–2021. MMWR Morb Mortal Wkly Rep. 2023;72(15). Updated April 2023. Accessed 6 March 2026. https://www.cdc.gov/mmwr/volumes/72/wr/mm7215a1.htm 
2. The state of innovation in pain and addiction, 2017–2022. Biotechnology Innovation Organization. BIO industry analysis. Published February 2023. Accessed 6 March 2026. https://go.bio.org/rs/490-EHZ-999/images/BIO_The_State_of_Innovation_in_Pain_and_Addiction_2017_2022.pdf 
3. Zou K, Wong J, Abdullah N, et al. Examination of overall treatment effect and the proportion attributable to contextual effect in osteoarthritis: meta-analysis of randomised controlled trials. Ann Rheum Dis. 2016;75(11):1964 1970.
4. Stone AA, Obbarius A. et al. High resolution, field approaches for assessing pain: ecological momentary assessment. Pain. 2021;162(1):4 9.
5. GlobalData Pharmaceutical Intelligence Center. Clinical Trials database. Accessed 30 September 2025.
6. Gomeni R, Bressolle Gomeni F, et al. Artificial intelligence approach for the analysis of placebo controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo. Transl Psychiatry. 2023;13:141.