When asked how Artificial Intelligence (AI) will truly move society forward, most experts include cancer care in their answer. From accelerating diagnosis to predicting the impact of drugs, there is a wide range of potential use cases for AI in oncology – but few have yet reached widespread adoption.
This article explores why that is the case and reveals four ways we believe cancer centers will use AI in 2025.
AI in Cancer Care: Where Are We Now?
Nearly all oncologists believe AI will help detect and treat cancer in the future, but the road to adoption will be long and complex. Around 9% of US radiologists are currently using AI, despite this being the field with the most devices that have gained regulatory approval.
There are several factors driving slow adoption, including:
- Regulatory Challenges: While the number of AI-based oncology solutions is rising, there is an ongoing conversation within the research community about regulatory scrutiny. How safe and effective will these solutions be upon widespread adoption?
- Ethical Concerns: For example, nearly 85% of oncologists believe AI must be fully explainable before being used in a clinical setting. There are also questions about ethics and patient choice: should patients know AI is being used during their treatment?
However, there is good reason to think these issues will not impede progress. The market for AI-based oncology tech is expected to nearly triple by 2029, and there are several powerful use cases that are likely to grow rapidly in the near future.
4 Ways AI Will Influence Cancer Care in 2025
Our experts predict at least four AI use cases will continue to grow in the next 12 months:
1. Cancer Screening
Cancer screening has been one of the most talked-about applications of AI within oncology, with proponents claiming the technology will enable faster and more precise detection. Researchers at Imperial College London found that AI could help detect 13% more breast cancer cases, which explains why AI-assisted screening technology has already been rolled out in some European countries.
Such technology is not standardized across the US, but the coming months could see an increase in adoption. The FDA has now approved nearly 600 products and devices for this purpose, and a growing number of organizations will see adoption as an opportunity to establish or maintain their status as leading cancer care providers.
2. Registry Data Analysis
Many cancer centers are “overrun at all levels,” - and the number of cases they must handle is only expected to grow in the coming years. This has created an unmanageable backlog for many registries, leading to:
- Excessive caseloads: A growing number of people are forced to pay for expensive outsourced support in order to stay on top of their caseload.
- Missed Opportunities: Many registries lack the time or resources to make full use of their data and end up missing valuable use cases.
AI-based technologies like Inspirata’s E-Path and E-Path Plus leverage natural language processing (NLP) to ingest and analyze huge volumes of structured and unstructured data from a wide range of sources. This allows them to find reportable cancer cases and pre-abstract cases in seconds – eliminating a large volume of work for ODSs and helping to combat backlogs.
3. Personalized Treatment
Cancer treatment planning could be revolutionized over the next few years as AI-based technologies unlock deeper insight into individual patients – and allow for scalable, personalized treatment plans that respond to the specific patient’s responses.
For example, patients in ‘maximum tolerated dose’ (MTD) therapy may develop rug resistance. However, researchers at the University of Oxford introduced a novel framework that uses AI to create adaptive therapy schedules for individual prostate cancer patients – and could potentially double the time to relapse compared to MTD or non-personalized treatment breaks.
4. Clinical Trial Matching
Clinical research is essential to develop more effective cancer treatments, but it is held back by poor trial accrual – forcing many trials to be abandoned or lack generalizability. At the heart of this problem is the inability of trial recruitment teams to effectively view patient populations and identify suitable candidates.
AI-based technology will help resolve this issue in 2025, with solutions like Inspirata’s Trial Navigator equipping cancer centers with a number of vital tools to fill more clinical trials. Using an NLP engine trained on over two decades’ worth of cancer data, the tool helps organizations:
- Improve Trial Design: Principal Investigators can see how changes to their inclusion criteria will impact trial feasibility – all in real-time.
- Accelerate Matching: Providers and recruitment teams can generate a “Match Score” to determine how well-suited a patient is for a specific trial – even at the point of care.
Better still, the tool features built-in explainability features – allowing users to quickly check which data was used and how the tool came to its decision. This helps to mitigate concerns around AI safety expressed within many cancer centers.
So while oncologists will require training and guidelines to use these features, it will quickly repay that investment. Trial Navigator will make running cancer research far easier in 2025, as cancer centers will be able to set up their trial with confidence that they can meet their accrual targets – and won’t end up disappointing their patients or pharmaceutical partners.
Want to explore how it could help you design, fill, and complete more clinical trials?
