Implementing Artificial Intelligence in Oncology: Enhancing Diagnostics and Treatment Planning
Introduction
According to latest findings by the WHO, 1 in 5 people develop cancer, and 1 in 9 men and 1 in 12 women succumb to it. This burden can be attributed to late-stage diagnosis which influences patient outcomes drastically. Delayed detection of cancer limits available therapies, and onset of therapy. It is seen that with every month of delay in therapy, the risk of death increases by 10%. Tumors vary phenotypically and genotypically. Thus, one solution may not fit all. A more personalized therapy that is based on genetic or molecular features of a tumor is necessary.
Additionally, trying to reduce treatment toxicity, and drug resistance, while maintaining its efficacy is also a long-standing challenge. In 2020, the total cost of treatment in the US alone was estimated to be $209 billion. Another challenge is that accessibility to treatment is not fair and support for patients following treatment like counseling, monitoring for recurrence, and improving Quality Of Life (QOL) is also lacking.
Most of the above challenges can be dealt with by collaborative efforts of policymakers, researchers, healthcare professionals, NGOs, and medtech companies using AI-based innovations. With each passing day, AI adoption in oncology is on the rise, to tackle the above challenges.
Benefits of AI in oncology healthcare
AI has the potential to revolutionize oncology care in the following ways –
1. AI in diagnosis
AI uses algorithms like Convolutional Neural Networks (CNN) which recognize patterns from gigantic amounts of images like histopathological slides, mammograms, CT scans, etc. This has pushed forward digital pathology in the diagnosis of cancer. Machine learning (ML) algorithms also analyze genomic datasets to pinpoint mutations linked to various cancer types, thus aiding in identifying the likelihood of a person developing a particular cancer. ML models are also trained to assess patient clinical records to identify early signs or risks of cancer. All this aids the physician in making accurate, consistent, and swift diagnoses.
2. AI in treatment planning and personalized care
Deep learning algorithms analyze patient data like their past and current medical history, demographic details, laboratory records, anatomy, and details about the tumor-like its size, location, shape, molecular profile, and genomics. They can also recognize patterns in information regarding similar cases, treatment response rates, and published cancer guidelines to provide treatment recommendations for chemo and radiotherapy.
They match cancer patients to appropriate clinical trials, predict prognosis more accurately, assess the possibility of adverse events during therapy, aid treatment de-escalation/escalation, predict the likelihood of survival at different time points, and determine which patients are suited for palliative care. In this way treatment is tailored to each individual and the physician is guided throughout in planning the most specific and effective treatment plan.
3. AI in reducing cost
As mentioned above, AI algorithms analyze vast medical datasets and recognize patterns that aid early diagnosis. When cancer risk is predicted, or cancer is detected early, it reduces the financial burden associated with advanced-stage therapies. AI can personalize treatment that avoids costs that come with the use of unnecessary treatments. AI matches cancer patients to available clinical trials, accelerating the development of new drugs and reducing the cost. AI can predict possible treatment complications and adverse events thereby reducing the cost of managing these events. It also provides facilities for remote consultations, and follow-up appointments, which reduces the need for in-person visits, thus minimizing costs.
4. AI in addressing accessibility problems
In developing and developed countries there seems to be an inequality in access to medical care based on socio-economic divisions. This is unacceptable as good health is every individual’s human right. AI-powered telemedicine platforms provide easy consultation with qualified oncologists via video calls that help people living in remote areas as it reduces the number of in-patient visits. AI-driven clinical decision support systems guide non-specialists and primary care providers of patients in limited resource settings, where there is shortage of trained pathologists and radiologists. AI-powered mobile phone applications help self-monitoring of symptoms and monitor if doses of prescribed drugs have been taken on time or not which improves adherence to cancer treatment.
5. AI in providing post-treatment support
After a cancer patient has completed his or her treatment and survived, the trial doesn’t end there. There is a lot of post-treatment care that the patients require like counselling, monitoring of reoccurrence of cancer, and facilities to improve QOL especially post a major surgery like tumor removal. AI-driven symptom monitoring systems help cancer survivors manage treatment-related side effects like pain, fatigue, and nausea and also help detect any cancer activity in the body. AI provides personalized recommendations for medication adjustments, and supportive care based on patients’ symptoms and treatment histories. It also provides personalized lifestyle intervention programs like regular exercise, balanced nutrition, smoking cessation, and stress management, to reduce cancer recurrence risk. AI-driven chatbots and virtual support groups provide emotional support, counseling, and resources for coping with psychological challenges such as anxiety, depression, and fear of recurrence.
Challenges in implementing AI & machine learning in oncology
There are always obstacles faced while implementing new ideas and the same applies to AI. AI/ML algorithms require large, high-quality datasets for training and validation. Ensuring data privacy and security, while aggregating and curating diverse datasets is challenging. Algorithms may exhibit biases due to imbalances or inaccuracies in training data, leading to disparities. AI-based diagnostic and therapeutic tools require clinical validation and regulatory approval to ensure their safety, efficacy, and clinical utility. Addressing these challenges requires interdisciplinary collaboration among clinicians, researchers, policymakers, industry stakeholders, and patients to develop clinically impactful AI solutions.
The future of AI in oncology
AI extracts quantitative features from medical imaging data like CT scans and MRIs and correlates these features with genomic data to predict tumor behavior, treatment response, and patient outcomes. AI algorithms predict patient responses to immunotherapy based on tumor immune profiles, genetic markers, and other parameters. By identifying patients who are most likely to benefit and predicting treatment resistance, AI improves healthcare. AI algorithms integrate data from multi-omics (genomics, transcriptomics, proteomics, and metabolomics) to comprehensively characterize tumors and identify novel therapeutic targets. Thus, AI holds the potential to alter the medical landscape.
About us:
Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With years of expertise in healthcare management and AI, we have collaborated with the CancerX community to provide AI solutions to accelerate innovations in the field of oncology.