How can AI personalize cancer treatment plans?

AI knowledge can be a very valuable tool in collecting real time patient data and analyzing the results from cancer patients. Since every human being’s immune system would react differently to the mutation in genes and alterations in the body, there is a great value in collecting physical, pain related emotional and mental data from patients to help target the appropriate treatment process for a disease labeled as one when it has so many causes, manifestations and treatment processes to help patient prevail over it. The mechanism of action is so different for each organ of the body that understanding and employing precision medicine is the key with real cases of patients suffering from this debilitating disease without using hit and trial.

Deep learning has contributed in cancer diagnosis with their applications in Medical imaging analysis. For instance, a study in this research showed that a CNN (Convolutional neural network) model outperformed human radiologists in detecting breast cancer in mammograms. Another, study showed that a Generative AI model, GANs (Generative adversarial network) can generate high-resolution brain MRI images, which helped improve the accuracy of brain tumor segmentation.

Even simple classification models can offer valuable insights. For instance, classifying cell stiffness in malignant cells can provide crucial information about the types of cancer that require different treatments.

AI can suggest clinicians : 1. Which treatment will be most effective for primary tumor and metastases (surgery, chemotherapy, radiation therapy or immunotherapy), 2. Which treatment will have minimum side-effects for the patient, 3. Which diet will be best for the patient recovery, 4. Discontinuing which habits can best benefit the patient, 5. Which supplements or supportive therapies can best benefit the patient, and 6. How patient prognosis will change, if any of the above factors change.

Machine learning has been used in genomic precision oncology for years; and advancements in AI will supercharge our ability to find trends in data and guide therapies. But at the end of the day, any treatment you administer to patients will be guided by a one-size-fits-all model based on aggregate date from external sources—meta analyses, clinical trials, etc. None of which can predict the patient's own response to treatment. There needs to be a way to observe the response of each individual patient's cancer cells to various treatments. Lionheart Biotech has developed a method of finding the most effective treatment for each patient by testing thousands of potential treatments on a tiny sample of a patient's tumor—all in just 24 hours.

Patients can be categorized based on their medical diagnoses as well as other clinical, genetic, and environmental factors using AI predictive classification algorithms. Consequently, generating a treatment plan and estimating the prognosis for cancer.

AI will be able to help cancer patients and their doctors be more aware of the increasing numbers of cancer growth causing mutations and the precision cancer medicines being developed to target and treat them.

Generative AI can be applicable to provide personalized guidance to the patients, track their health metrics, and patients can offer real-time feedback on the side-effects of the targeted therapies. Thus optimizing the treatment plan.

Generative AI can be used for the cancer prevention research to generate new variations in patient data, however human intervention is a must to validate.

Cancer is preventable in many cases. Like other complex diseases, cancer also has many risk factors like genetics, lifestyle and environment. AI has been greatly beneficial in putting together pieces of information that can help guide these strategies. At Mapmygenome we have connected the dots between genetic markers, blood based tests, lifestyle and more. This wouldnt be possible without AI. The outcome helps in guiding patients and physicians.

Cancer like any disease process is a multifactorial one. Management of the optionality and success rates of treatment options/different combinations can best be refined by AI. Novel combinations based on broad biomarker datasets coupled with device surveillance can provide consistent monitoring rather than episodic. The cautionary tale with using chatbots and similar AI is how they were trained. Like humans - training and experience matters.

Machine learning tools that perform mammography reading have the potential to reduce the work look for physicians and perhaps reallocate them to direct patient care where there is a shortage of oncologists.

AI can enhance patient self-service, engagement, and navigation. For instance, 1. Virtual assistants can engage in natural language conversations with patients, providing symptom assessments, personalized health information, and guidance. It can simplify complex medical language into summaries that can be easily understood by patients and translated into multiple languages. 2. AI can help improve medication adherence by providing reminders, personalized dosage instructions, and monitoring medication intake. 3. AI chatbots or virtual companions can engage in empathetic conversations, offer coping strategies, and provide resources for emotional and mental health support.

By analysing vast amounts of patient data, AI can discern subtle patterns, genetic markers, and treatment outcomes. This facilitates the tailoring of treatment plans to suit individual patients, considering factors such as genetic predispositions, tumor characteristics, and past treatment responses. AI's ability to rapidly process complex data empowers oncologists to make more informed decisions, ultimately leading to more effective and precise treatments. With continuous advancements, AI promises to play a pivotal role in not just personalising cancer treatment plans, but also in driving advancements towards more targeted, efficient, and patient-centric care.