AI means computer systems made to act like human intelligence. These systems can find patterns, make guesses, and learn from data. In cancer care, AI methods like machine learning and deep learning look at complex medical information, such as images and lab reports, to help doctors make decisions. AI tools do not replace doctors. Instead, they support the usual ways of diagnosing cancer.
A review by Claudio Luchini and others found that 71 AI devices have been approved by the US Food and Drug Administration (FDA) for cancer use. Most of these devices (over 80%) focus on diagnosing cancer. Radiology, which uses imaging like X-rays, mammograms, and CT scans, makes up more than half (54.9%) of these approved AI devices. Pathology, which studies tissue samples, makes up about 19.7%. Breast, lung, and prostate cancers are the main types these AI tools focus on. Breast cancer is 31% of the tumor types these devices analyze.
Breast cancer is one of the most common cancers among women in the United States. Early detection through screening programs like mammography helps improve treatment results. However, reading mammograms can be hard and sometimes unclear, leading to overdiagnosis or missed cases. AI can help radiologists by giving steady, data-based readings.
Studies from Europe, including Sweden, showed that AI programs can do as well or better than human radiologists in screening for breast cancer. AI reduces the work for radiologists without lowering accuracy. This is important since there are fewer oncology doctors now. The UK’s National Health Service (NHS) has paid for AI tools in many trusts to speed up the analysis of mammograms and CT scans. This shows growing use of AI in clinics.
In the US, AI shows promise but faces challenges. These include fitting AI into current healthcare work and training clinicians well. Research from Kaiser Permanente Northern California found that routine breast exams by doctors found only a small part (6.8%) of breast cancer returns. Meanwhile, 69.4% of returns were found through symptoms noticed by patients. This suggests AI imaging could help monitor patients better than physical exams alone. It could find returns earlier and improve survival.
Lung cancer screening is an important area where AI can help make diagnosis better. Low-dose CT scans are now often used to check for lung cancer in people at risk. Just like with breast cancer, AI programs analyze lung CT scans to find cancer earlier and more accurately.
In 2023, the European Union updated its cancer screening rules to include lung cancer screening officially. They noted that AI can reduce work for radiologists while keeping or improving accuracy. The UK NHS recently funded AI to check chest images. This trend may soon come to the US healthcare system.
One economic study from the US said AI-assisted low-dose CT scans for lung cancer screening can save money. Savings could reach up to $1,240 per person screened. This comes from finding cancer earlier and making fewer mistakes. Early diagnosis can cut down on later, expensive treatments that are harder to do.
However, evidence about AI’s effect in all clinical settings is mixed. AI helps less experienced clinicians the most. But in cases where cancer suspicion is high, AI might delay diagnosis. Because of this, doctors must still review AI results carefully.
Prostate cancer is a common cancer among men in the US. Early diagnosis is important to improve patient results. AI has been used to study MRI scans and lab tests to give clearer risk assessments and help decide when a biopsy is needed.
The European Union has started pilot programs for prostate cancer screening using AI. These include community outreach and mobile MRI units. The US does not yet use these methods widely, but AI technology is growing fast and may soon impact prostate cancer detection and care.
Recent studies show AI tools could improve diagnosis accuracy, especially for early-stage cancers where usual images and lab markers are not clear. AI’s ability to combine complex biological data, like DNA, RNA, and proteins, offers chances to make prostate cancer screening more personal. Still, gathering and combining this data remains difficult.
AI can improve workflow in healthcare beyond just reading images and lab results. AI can also help automate front-office jobs like scheduling appointments, answering phones, handling patient questions, and managing information.
Companies like Simbo AI focus on automating phone answering and providing AI-driven phone services for healthcare providers. Automating routine communications lets medical staff focus more on clinical work, such as faster cancer diagnoses.
Using AI for office tasks in cancer clinics can:
Adding AI for these tasks helps improve the overall efficiency of cancer care. This is especially important in US healthcare, where managers and IT leaders must balance cost, quality, and work demands.
US healthcare providers face pressure to use AI tools that improve both patient care and finances. FDA approval of AI devices for cancer diagnosis helps practice managers feel more confident in investing in these tools.
Breast, lung, and prostate cancers benefit most from AI support because these cancers occur often, have screening programs, and can be treated better if found early. But practices also need to plan for staff training, making different data systems work together, and fitting AI tools into existing systems.
Patient education about AI is also important. Studies from Kaiser Permanente show patients often find cancer symptoms themselves. So AI tools should support, not replace, patient communication and involvement.
As AI technology grows, US policies and payment systems will have to change too. This is needed to make sure AI-assisted diagnostics remain accessible and affordable for healthcare providers.
As AI grows in cancer diagnostics and healthcare administration, US medical practices have the chance to improve diagnosis accuracy, find cancer earlier, reduce doctor workload, and improve patient communication. Using AI tools in both clinical and office tasks may raise the quality and speed of cancer care. Practice managers and IT leaders should carefully manage these changes to get the best results for patients and healthcare teams.
AI is significantly reshaping oncology by improving cancer patient management, particularly in diagnostics, where it has the largest influence on clinical practice.
Breast, lung, and prostate cancers are currently experiencing the biggest advantages from AI-based devices in clinical practice.
Seventy-one AI-associated devices have received FDA approval for use in oncology-related fields, primarily in cancer diagnostics.
Machine learning refers to a machine’s ability to learn patterns from data, whereas deep learning is a machine learning method utilizing complex networks for enhanced prediction.
AI integrates multi-omics data with high-performance computing and deep learning to improve cancer detection, treatment, and follow-up strategies.
AI is predominantly used as an integrative tool in cancer diagnostics, enhancing traditional methods rather than replacing them.
Future challenges include exploring applications beyond diagnostics, including drug discovery, therapy administration, and addressing needs for rare tumors.
AI has evolved from simple rule-based systems to complex algorithms capable of mimicking human cognitive processes in various fields, including oncology.
FDA approval signifies that AI devices meet safety and effectiveness standards for use in clinical settings, highlighting their importance in patient care.
The development of AI for rare tumors remains a challenge due to the need for larger data sets, but these tumors are crucial for overall advancements in precision medicine.
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