The Growing Influence Of AI In Cancer Care
No doubt the fight against cancer is an ongoing battle. Technological advancements are there, but the challenges are still significant. But the way AI is helping practices diagnose conditions, plan treatments, and manage finances while adhering to tough regulations is already remarkable. And this is just the beginning.
What Gave AI The Head Start
As of today, oncology stands as one of the most thoroughly researched healthcare specialties. There is plenty of scientific literature available on cancer. Decades of clinical trials, papers, and studies provide an indefinite number of data points. But all this data needed a system, as it is beyond human grasp.
AI or, to be precise, LLM happens to be the system that can find, absorb, and recall all these data points. It works at an immeasurable efficiency to aggregate and parse highly complicated datasets without making errors typical of humans. With these abilities, AI can:
- Process immense amounts of unstructured and structured data
- Identify nuanced patterns
- Predict progressions and trends
- Perform complex and multi-dimensional analy
Not only do these functions help bring accuracy and efficiency to existing processes, but they can also augment cancer research. AI already has the ability to reason, which means it can hypothesize scenarios and solutions to fuel advanced research. And it is just a matter of time when it will be able to help streamline studies, trial designs, and recruitments. This will create a significant impact that will lead to optimum cure.
The Role Of AI In Different Areas Of Oncology
In prevention
Prevention and early detection have always been the most effective strategies in cancer care. But it doesn’t mean in any way that prevention is easy. Cancer can occur due to an indefinite number of reasons. Also, it is almost impossible to say with certainty when and how the first signs might occur. This makes it extremely difficult to know when to recommend screening.
Another significant challenge is public access to screening. The reasons could be many, including proximity to cancer care facilities, medical literacy, or socioeconomic barriers.
AI promises to be a significant breakthrough in the removal of all these obstacles. As mentioned above, it can predict scenarios based on available data points regardless of their sheer numbers. It is a cancer prevention revolution in line, just waiting to happen in the near future.
In fact, 2023 research already demonstrated the AI’s ability in predicting the incidences of pancreatic cancer. The AI system used disease codes and the condition’s timing of occurrence from millions of patients. It was able to predict which patients were more likely to develop the disease. And according to the reports, the predictions were at least as accurate as current genetic sequencing tests, which can be performed only on a small number of patients in a large data set.
In other words, the AI system could accomplish what usually requires an expensive procedure involving millions of data points.
Again, it is just the beginning. AI will only become smarter in disease prevention with time. And it won’t be an exaggeration to suggest that this growth will be exponential.
In Diagnosis
On the surface, diagnosis may seem significantly more complicated than prevention. But fundamentally, it is not that different. The datasets that AI can use to help prevent cancer can also provide actionable details about diagnosis.
That said, we are still far away from deploying friendly robots instead of doctors. However, the existing models can recall information, compare scans and charts, and make assumptions like seasoned practitioners. The significant advantage of such models is that they can limit the need for invasive diagnostic procedures.
One of the examples is of a case of a woman with a persistent thyroid lump. Her doctor examined that lump through ultrasound and performed a biopsy afterwards. The growth turned out to be benign.
She then visited another practitioner for a second opinion. The doctor came to the same conclusion, but he diagnosed the problem using AI-based ultrasounds. She could have avoided the invasive test and the waiting time for the report had she visited the second practitioner first.
This was one of many cases where AI provided accurate diagnoses. It demonstrated the AI’s ability to accurately spot tumor-like growth in patients using imaging tests like MRIs and ultrasounds. This enables oncologists and radiologists to start deeper examinations right away.
In Treatment
AI has become a practical option in cancer treatment and intervention. This notion is supported by a 2023 study, which examined the AI’s influence on precision medicine and treatment plan development.
The AI system in the study predicted treatment effects for tumor patients. Using these predictions, it personalized treatments based on the patients’ unique needs. The researchers supplied the agent with deep-level information on genomics, which created complex parameters for the algorithm to work on.
But the role of AI is not limited just to data-based assessments. Its operations are becoming real-time. AI algos can optimize radiation doses, provide active support during surgeries, and offer adjustments to active treatment plans.
In RCM
Oncology billing has traditionally been the number one reason for high administrative costs. The reason is it is a complex process from start to finish as it involves:
- Complicated pre-approvals
- Assessing a claim for its chances of approval or denial
- Tricky denial management
- Appealing
- Payment posting and reconciliation
This entire process makes up a significant administrative burden and extensive expenditures. The patient care gets compromised as a result.
AI and machine learning technologies come to the rescue. Today, many healthcare IT vendors and corporate RCM companies offer smart solutions that streamline each step of the billing process. These solutions use years of historical data and EHRs to assist with claim submission and reimbursements. They also provide analytics based on established metrics to help practitioners spot problems and identify trends.
Not only do AI platforms ensure procedural accuracy, but they also somehow automate the RCM process. This translates into efficient operations with a reduced risk of denials.
One major advantage of AI-assisted RCM is that it supports value-based care. The reason is rather logical. AI platforms relieve physicians of the clerical burden, which allows them to focus on patient care. Secondly, many platforms use workflows that abide by MIPS protocols. This helps practices to target the incentives tied to quality care.
What Are The Challenges?
Despite Hollywood’s depiction of AI as a faultless entity, it still has genuine limitations. In the case of healthcare, these limitations become more pronounced due to the sensitivity of patients’ data.
The top concerns include the following:
- Data privacy: The involvement of AI means you entrust protection-worthy data to tools. This increases the risk of that data getting sold or shared. Or at the very least, the vendors might use it as a market research opportunity.
- Scalability and reliability: We are yet to see a widespread adoption of AI systems across the entire oncology care ecosystems. And until that happens, problems like interoperability and refinement of data quality will persist.
- Social effect: AI is a creation of humans, some of whom might not be as ethically responsible as others. This means no matter how profoundly someone claims their AI to be unbiased, fairness issues will still arise. But that’s not to say there is no solution. AI is in its infancy. The formation of centralized authorities is very much possible to ensure AI’s fairness.
AI Is The Way To Go
AI is benefiting cancer care and research in ways unimaginable. It has helped oncologists to ditch guesswork and develop personalized care plans using accurate data. Its contribution to cancer research is already remarkable. It is also helping oncology practices with complicated revenue management operations.
In other words, AI has reshaped cancer care almost entirely. Sure, challenges exist, with some of them being crucial. But the prospects of advancements suggest we will move past these concerns in a not-so-distant future.
Subscribe to Our Newsletter!
Subscribe to Our Newsletter!
Enter Your Email Address. We Promise We Won't Spam You