Increasing Speed of Treatment Planning Systems and the Integration of AI

Dattoli Cancer Center

September 16, 2022

As the adoption of AI continues to accelerate, several challenges remain. To overcome these challenges, organizations need to reach digital maturity. That means developing robust governance and data maintenance processes. Moreover, they must implement modern software disciplines like Agile and DevOps. In addition, the “last mile” problem is a significant hurdle from a scale perspective. To address this issue, organizations should consider a few key steps to make adopting Treatment Planning Systems and the Integration of AI as seamless as possible.

Increasing speed of treatment planning systems

AI is a powerful tool for health professionals to find better treatments and accurately diagnose patients. It can access and match millions of diagnostic resources and augment a doctor’s clinical knowledge. As a result, it could reduce the need for human physicians. However, there are many challenges associated with using AI for health care.

Large companies with deep digital experience will likely be the early movers in AI adoption. These companies will likely be able to leverage their existing data, digital expertise, and technical skills. As a result, the sectors that will grow the fastest will likely be the ones that invest in AI.

Integration of AI in radiotherapy

AI is an essential tool in radiotherapy, and integrating AI in this field can improve patient outcomes. For example, a recent study published in Medical Physics shows how AI can quickly and accurately recalculate radiation doses based on patients’ anatomy. Conventional recalculation can take up to ten minutes, and AI can produce an optimal plan in as little as five minutes.

While the integration of AI into radiation therapy is still in its early days, it can improve treatment accuracy, personalization, and efficiency. However, there are multiple hurdles before this technology is widely implemented. One of the biggest challenges is training AI to perform complex tasks and ensuring its effectiveness in clinical practice. The industry must continue exploring the application of AI and its potential benefits in radiotherapy.

Challenges for smaller firms

There are several challenges for smaller firms when implementing treatment planning systems. In addition to implementing a new system, they must also figure out how to maintain high-quality standards. The EPA recently announced $3,089,894 in funding to 30 small firms for innovative technologies. These include automated waste sorting systems at the point of disposal, a system to detect and destroy airborne viruses and bacteria, and a monitoring system for methane emissions and concentrations.

Impact on accuracy

Automated clinical decision support systems (CDSS) have been around for 40 years. But the clinical adoption of these systems has been a mixed bag. One factor that has increased interest in physician education is AI. This will hopefully expedite AI integration into clinical workflows.

For example, AI can help doctors find cancer treatments that are more precise than ever. In addition, experts can use AI to understand tumors’ characteristics better and predict the potential effectiveness of new drugs. AI can also help detect cancer abnormalities in patients’ body chemistry.

However, several issues must be addressed before AI can be used effectively in medicine. For example, identifying gene mutations in cancer patients using noninvasive techniques is still a significant challenge. However, the NCI recently supported a multidisciplinary team to develop a DL method for identifying IDH mutations in gliomas from MRI images. These advances in AI could make it easier to find gene mutations in the future.

Impact on survival Treatment Planning

Researchers are looking at how AI can improve the diagnosis and treatment of cancer by recognizing specific gene mutations in tumors. AI can be trained to identify these mutations from images of tumors, and some researchers are using the technology to develop noninvasive methods for this purpose.

AI has several healthcare applications, from improving workflows to developing new treatments and therapies. AI can also help predict the patient’s risk of hospital admission and detect cancer early. The report outlines some potential applications and highlights the challenges and opportunities healthcare providers must consider when applying AI in healthcare.

Treatment planning systems based on AI can better use genomic data to help doctors develop new cancer treatments quickly. Currently, doctors are unable to predict the risk of disease by analyzing molecular phenotypes, but AI can identify these features and develop customized therapies for patients.