Artificial intelligence (AI) is becoming one of the most powerful tools in modern cancer care. It helps doctors make faster decisions, detect cancer earlier, plan better treatments, and monitor patients more safely. AI does not replace oncologists; it supports them by providing extra information and reducing mistakes. Today, AI touches almost every part of oncology, from screening to long-term follow-up.

1. Early Detection and Screening (Using Radiomics)

Early detection is the most important factor in improving cancer survival, and AI is transforming this area through a technique called radiomics. Radiomics allows computers to study medical images such as CT scans, MRIs, and mammograms in extremely great detail.
A radiologist might see a small shadow or nodule, but AI can analyse hundreds of features like texture, shape, edges, and brightness. This helps it spot very tiny changes that might suggest early cancer. For example:
AI tools can detect lung nodules long before symptoms appear. In breast cancer screening, AI improves detection in patients with dense breast tissue. AI models can also analyse changes over time and alert doctors if a lesion has grown or changed shape.
Researchers are also testing AI in blood tests, liquid biopsies, and even breath samples to detect cancer-related molecules early. With these tools, screening becomes more accurate and more accessible, helping reduce late-stage diagnoses.

2. Personalisation of Therapy (Using Genomic Profiling)

Every patient and every tumour is unique. AI plays a major role in making treatment personalised, meaning it is chosen based on what will work best for that specific person. A key part of this is genomic profiling, which studies the genes and mutations inside a cancer cell.
With genomic data, AI can compare a patient’s tumour to thousands of others and see which treatments were effective for similar cases. It looks at:

Gene mutations
Protein signals

Patterns in tumour growth, the immune environment around the tumour. Based on this information, AI may suggest: which targeted therapy might work best, whether immunotherapy is likely to help, which chemotherapy has the highest chance of success how radiation plans can be designed to protect healthy tissue. AI also helps create more accurate radiation therapy plans by predicting how the tumour and organs will respond. This means safer treatments, fewer side effects, and better results overall.

3. Prediction of Toxicity and Outcomes (Using Machine Learning Models)

Cancer treatment can cause serious side effects, and predicting who is at risk is not always easy. AI helps by using machine learning models in computer systems that learn from thousands of previous patient cases. These models study patterns in:

Lab results
Medical history
Imaging
Genetic features
Previous treatment responses
With this information, AI can predict risks such as:
Severe drop in blood counts
Kidney or liver injury
Radiation pneumonitis
Nerve damage from certain chemotherapies
Immune-related side effects

AI can also estimate how well a patient is likely to respond to a treatment. For example, it can help predict the chances of tumours shrinking, the likelihood of cancer coming back, or expected survival outcomes. This allows doctors to take preventive steps such as adjusting medicine doses, giving extra supportive care, or choosing safer drugs before problems become serious.

4. Patient Monitoring and Safety (Using Biosensors and Wearables)

AI is also improving the way patients are monitored during and after treatment. Many cancer patients now use biosensors or wearable devices that continuously track body signals. These can measure:

Heart rate
Oxygen level
Temperature
Activity level
Sleep patterns

AI analyses the data in real time and looks for changes that might indicate early trouble, for example, infection, dehydration, or a severe reaction to treatment. This helps doctors intervene earlier, sometimes preventing hospital admissions.
Wearables are especially useful for patients on chemotherapy or immunotherapy, where problems can develop suddenly. Remote monitoring also reduces the need for frequent hospital visits, saving time and improving quality of life.
By catching complications early and reducing human errors, AI makes cancer care safer, more reliable, and more patient-friendly.

Conclusion

Artificial intelligence is becoming a vital part of modern oncology. Through radionics, genomic profiling, machine learning models, and biosensors, AI helps detect cancer earlier, personalise treatments, predict side effects, and monitor patients more safely. As technology continues to advance, AI will help create a future where cancer care is more accurate, more efficient, and more humane, improving both survival and quality of life for millions of patients.