ChatGPT is a culmination of Reinforcement Learning optimization strategies, specifically Proximal Policy Optimization (PPO). OpenAI leveraged AI trainers to rank the model and shape rewards based on model ranking. Make no mistake: reinforcement learning requires constant iterations, trial and error, rewarding unintended behaviors, etc. The computational barrier to entry is costly in compute costs and time to train. However, it is one of the most effective conversational AI's to date.

Researchers utilized five different machine learning approaches to accurately spot lymphedema--a negative side effect of breast cancer treatment--which may help detect it earlier and improve treatment. The study was published in the May edition of the journal mHealth. "Using a well-trained classification algorithm to detect lymphedema based on real-time symptom reports is a highly promising tool that may improve lymphedema outcomes," said lead author Mei R Fu, PhD, and associate professor of nursing at New York University in an NYU release. A web-based tool collected information from 355 women who had undergone treatment for breast cancer, including surgery. Participants shared demographic data and clinical information and were asked if they were experiencing any 26 different lymphedema-related symptoms.

A new study led by NYU Rory Meyers College of Nursing shows that machine learning--combined with the collection of real-time symptom reports using a mHealth system--can provide early detection and help patients to receive timely intervention to effectively manage lymphedema. Lymphedema, which has no cure and comes with lifelong risk, is the build-up of lymph fluid that causes swelling in the arms or legs of patients. In the study of 355 women from 45 states who had undergone treatment for breast cancer, the performance of five machine learning algorithms were evaluated--artificial neural network (ANN), Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model and support vector machine. According to results published in the journal mHealth, all five machine learning approaches outperformed the conventional statistical approach. However, of the five, the ANN achieved the best performance for detecting lymphedema with accuracy of 93.75 percent, sensitivity of 95.65 percent and specificity of 91.03 percent.

It can occur after cancer surgery or as late as 20 years after, Fu and co-authors said. But within a decade of treatment, 41 percent of breast cancer patients experience it. Fu's team focused on machine learning since the technology excels in processing handfuls of data points that are independent from one another, just like lymphedema symptoms. Three hundred and fifty-five women who had undergone breast cancer treatment were included in the study, in which the researchers collected demographic and clinical information before asking patients whether they were experiencing any of 26 lymphedema systems. They said all five modalities identified lymphedema more accurately than the standard statistical approach, but the artificial neural network was the most successful, with 93.8 percent accuracy.

"Using a well-trained classification algorithm to detect lymphedema based on real-time symptom reports is a highly promising tool that may improve lymphedema outcomes," said Mei R Fu, PhD, RN, FAAN, associate professor of nursing at NYU Meyers and the study's lead author. Lymphedema is a build-up of lymph fluid that causes swelling in the arms or legs and is commonly caused by the removal of lymph nodes as part of cancer treatment. It can occur immediately after cancer surgery or as late as 20 years after surgery; a recent study found that more than 41 percent of breast cancer patients experienced lymphedema in their arms within 10 years of their surgery. Lymphedema is one of the most dreaded adverse effects from breast cancer treatment because of its chronic nature and debilitating symptoms, including arm swelling, heaviness, tightness, achiness, stiffness, burning, and decreased mobility. While there is no cure for lymphedema, early detection and intervention can reduce symptoms and keep it from worsening, although early detection remains a challenge.