At the confluence of big data, patient involvement in their own health, and systems medicine, healthcare is undergoing a profound revolution — we are seeing a new medicine take form that is predictive, preventive, personalized, and increasingly participatory. What’s more, with the advent of superior data processing power and advanced analytics, we can also see precision medicine making its way through. A specific medical field that has already greatly leveraged the potential benefits offered by artificial intelligence is Oncology.
AI has already had a significant impact on healthcare and continues to revolutionize medicine. Let’s explore a few ways that AI can impact and improve cancer care.
1. Wider Reach Screening & Early Detection
A good outcome depends on the quality of several factors, and detection is among the most important of them. Machine learning and AI can contribute to enhanced screening tools and even earlier detection, which are crucial to the approach to cancer patient care. Using advanced analytics and imaging data along with screening on a wider scale can improve physicians’ chances of catching very early signs of cancer and inform even better detection going forward.
For example, deep learning algorithms can be trained on large datasets of annotated images to identify potential cancerous lesions or tumors. These algorithms can then be used as a “second opinion” to support radiologists or pathologists in their diagnostic process. For instance, patients with ovarian cancer tend to have nonspecific symptoms, which are generally associated with other potential causes. By the time blood tests, biopsy analysis, and imaging contribute to a diagnosis, the cancer may already have advanced to a late stage. Consequently, survival rates for people with ovarian cancer are lower than those with cancers that happen to present symptoms that are more likely to prompt cancer screening. Advanced analysis of complex sets of patient data with the help of AI can help medical providers compensate for this and similar symptomatologic blind spots.
2. More Accurate Diagnosis & Prognostication
Making AI an integral part of oncological screening, as well as expanding the reach of screening services, can greatly improve diagnosis accuracy. This is a highly important factor in outcome quality for any medical condition, particularly in the case of cancer — an ailment that manifests with peculiar variety and which so often presents a chronological challenge.
Traditionally, tumor response to therapy has been notoriously difficult to predict, as this disease generally presents high heterogeneity. Given also its unpredictable pace of progression, taking the time to test various treatment approaches can mean that the tumor has time to advance before any courses of therapy take effect. However, connecting health data resources with advanced analytics can help identify treatment recommendations sooner that have the highest chance of success and ultimately improve patients’ chances.
3. Personalized Treatment Design
In addition to helping doctors better identify malignant lesions in their patients, artificial intelligence can contribute to designing personalized treatments with greater confidence. AI can analyze large amounts of patient data, including genetic information, medical history, and treatment outcomes, to develop personalized treatment plans. This can lead to more effective treatments with fewer side effects.
As noted previously, cancer care faces a highly heterogeneous foe that advances at an unpredictable pace. Streamlining diagnosis and treatment pathways with the help of advanced laboratory information management systems solutions and advanced analytics tools such as AI can give cancer care providers the upper hand.
Developing better-precision images of cancer pathology and analyzing imaging information along with multiple other patient data points such as medical history, blood test results, and genomic data can bridge the gaps we currently have in understanding the characteristics of any particular tumor. This, in turn, is essential in predicting which treatments and approaches to patient care may be most successful.
Additionally, AI can aid in identifying patients who may benefit from targeted therapies or immunotherapies. By analyzing genetic variations and molecular signatures, AI algorithms can match patients with specific genetic profiles to appropriate treatment options, maximizing the likelihood of successful outcomes.
4. Mining Cancer Care Monitoring Data for New Prognosis & Outcome Insights
The potential benefits of utilizing advanced computing and analytics tools, including artificial intelligence and machine learning, have been increasingly investigated concerning the multifaceted treatment decision-making process. As the analysis process refines AI’s performance, applying these systems to cancer care monitoring data can unlock invaluable insights into treatment response efficiency, remission, and relapse rates, any unsuspected and unintended effects on non-targeted areas of the patient’s body, and much more. AI can also simulate drug interactions with cancer cells, predict their efficacy, and identify potential combinations or repurposed drug that may have synergistic effects.
These capabilities can accelerate the development of new therapies and improve the chances of finding effective treatments for different types of cancer. Balancing treatment outcome risks with quality of life is very important to consider when deciding on which course of therapy to recommend for any patient. AI can mine crucial insights into how sufficiently similar malignant lesions have responded to various approaches, as well as how corrective courses of therapy have otherwise affected patients that present sufficiently similar biology and lifestyle data — in the case of aggressive cancer treatment, the precision of targeting is still far from exact and can cause harm elsewhere in the body, which still affects patient health and quality of life down the line, even if the treated cancer is resolved.
5. Reducing Costs and Preventing Unnecessary Invasive Exploratory Procedures
Systems medicine has done a lot to reveal how many data points can and should go into any medical approach to significantly improve patient outcomes. Through the use of advanced analytical tools such as artificial intelligence, we harness the power of farming billions of data points into models that can guide improvement in health and hopefully reduction of overall disease.
Specifically, increasing the precision of medicine even in the early stages of routine screening, can lead to significant reductions in overall healthcare costs both for providers and for patients, as it would remove the need for exploratory testing that has traditionally been done to gradually reach a diagnosis by systematic elimination — an often-burdensome undertaking that costs not just money, but also possibly critical time. Increasing the speed and efficiency of early diagnosis can make a huge difference in patient outcomes and treatment success, as well as avoiding procedures such as unnecessary biopsies in healthy people. AI has immense potential to contribute here.
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Overall, AI has the potential to revolutionize cancer care by improving early detection, personalizing treatment plans, accelerating the development of new treatments, accelerating drug discovery, and assisting doctors in clinical decision-making.
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