New AI Tool Predicts Risk for Chronic Pain in Cancer Patients

A third of cancer patients face chronic pain - a debilitating condition that can dramatically reduce a person's quality of life, even if their cancer goes into remission.

Although doctors have some tools for addressing chronic pain, figuring out who is most at risk for developing it is no easy feat. But a new study, conducted by researchers at the University of Florida and other institutions, uses artificial intelligence (AI) to predict which breast cancer patients are most at risk for developing chronic pain. The predictive model could help doctors address underlying conditions that contribute to making pain chronic and ultimately lead to more effective treatments.

"We want to understand the factors that lead someone from having cancer to having chronic pain and how can we better manage these factors," said Lisiane Pruinelli, Ph.D., M.S., R.N., FAMIA, the senior author of the new study and a professor of family, community, and health systems science in the UF College of Nursing. "Our goal is to link this information to some profile of patients so we can identify early on what patients are at risk for developing chronic pain."

The findings of the study were published on July 26 in the Journal of Nursing Scholarship. The authors included Pruinelli, Jung In Park, Ph.D., R.N., FAMIA, of the University of California, Irvine, and Steven Johnson, Ph.D., of the University of Minnesota.

The results showed that, when built with detailed data on more than 1,000 breast cancer patients, the AI model could correctly predict which patients would develop chronic pain more than 80% of the time. The leading factors that were associated with chronic pain included anxiety and depression, previous cancer diagnoses, and certain infections.

Implementing a model like this in doctors' offices would require integrating it into the electronic healthcare records systems that are now ubiquitous in clinics, which would take more research. The researchers said the rise of AI has the potential to help doctors tailor their treatments to a patient's unique disease characteristics.

"Now with the amount of data we have, and with the use of artificial intelligence, we can actually personalize treatments based on patient needs and how they would respond to that treatment," Pruinelli said.

The study was based on the large amount of data made available by the All of Us Research Program, a nationwide research campaign from the National Institutes of Health that seeks to collect anonymized healthcare records from 1 million Americans.

"This wouldn't be possible if we didn't have people contributing their data," Pruinelli said.

Park JI, Johnson S, Pruinelli L.
Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.
J Nurs Scholarsh. 2024 Jul 26. doi: 10.1111/jnu.13009

Most Popular Now

Stepping Hill Hospital Announced as SPAR…

Stepping Hill Hospital, part of Stockport NHS Foundation Trust, has replaced its bedside units with state-of-the art devices running a full range of information, engagement, communications and productivity apps, to...

DMEA 2025: Digital Health Worldwide in B…

8 - 10 April 2025, Berlin, Germany. From the AI Act, to the potential of the European Health Data Space, to the power of patient data in Scandinavia - DMEA 2025...

Is AI in Medicine Playing Fair?

As artificial intelligence (AI) rapidly integrates into health care, a new study by researchers at the Icahn School of Medicine at Mount Sinai reveals that all generative AI models may...

New System for the Early Detection of Au…

A team from the Human-Tech Institute-Universitat Politècnica de València has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence. The...

Generative AI's Diagnostic Capabili…

The use of generative AI for diagnostics has attracted attention in the medical field and many research papers have been published on this topic. However, because the evaluation criteria were...

Diagnoses and Treatment Recommendations …

A new study led by Prof. Dan Zeltzer, a digital health expert from the Berglas School of Economics at Tel Aviv University, compared the quality of diagnostic and treatment recommendations...

AI Tool can Track Effectiveness of Multi…

A new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS) has been developed by UCL researchers. AI uses...

Surrey and Sussex Healthcare NHS Trust g…

Surrey and Sussex Healthcare NHS Trust has marked an important milestone in connecting busy radiologists across large parts of South East England, following the successful go live of Sectra's enterprise...

Dr Jason Broch Joins the Highland Market…

The Highland Marketing advisory board has welcomed a new member - Dr Jason Broch, a GP and director with a strong track record in the NHS and IT-enabled transformation. Dr Broch...

DMEA 2025 Ends with Record Attendance an…

8 - 10 April 2025, Berlin, Germany. DMEA 2025 came to a successful close with record attendance and an impressive program. 20,500 participants attended Europe's leading digital health event over the...

Multi-Resistance in Bacteria Predicted b…

An AI model trained on large amounts of genetic data can predict whether bacteria will become antibiotic-resistant. The new study shows that antibiotic resistance is more easily transmitted between genetically...

AI-Driven Smart Devices to Transform Hea…

AI-powered, internet-connected medical devices have the potential to revolutionise healthcare by enabling early disease detection, real-time patient monitoring, and personalised treatments, a new study suggests. They are already saving lives...