How Artificial Intelligence is Revolutionizing Cancer Care Today and Tomorrow (Part 1)

 

Cancer remains the leading cause of international extinction, taking more than 10 million lives in 2022, according to my estimate. As the global burden continues to grow, healthcare structures are under considerable pressure to beautify every factor of most prevention, detection, prognosis, treatment and care transport.

Artificial intelligence (AI), which can process vast amounts of data and discover problematic patterns, is an effective ally in the fight against most cancers. By harnessing machine learning, deep learning, computer vision, natural language processing and other cutting-edge techniques, AI can augment human capabilities and push for more accurate, greener, customised cancer care.

In this multi-part series, we will discover the wide-ranging effect that AI is bringing to most cancer patients today through screening, diagnosis, treatment planning, and monitoring. We will also explore an emerging technology that could revolutionise cancer care in the coming years.

Early detection and screening

Catching cancer early, before it progresses and spreads, dramatically improves outcomes and survival rates. However, screening methods such as mammograms and colonoscopies have barriers to accuracy, and public adoption costs remain suboptimal. Artificial intelligence improves our screening and early detection capabilities:

Breast Cancer: Artificial intelligence systems analysing virtual mammograms have been shown to match or outperform expert radiologists in detecting breast cancer lesions. A landmark study in The Lancet tested how one deep-mastering model showed higher sensitivity and specificity than human experts.

Lung cancer: AI-assisted chest CT analysis can pick up on very small lung nodules and flag people with suspicious features that could indicate most lung cancers, even though radiologists may miss them. Multiple studies show superior overall AI performance.

Artificial intelligence is also used to examine genomic records, biomarkers, one's family history and various risk factors through machine learning models that generate customised screening suggestions against traditional one-size-fits-all recommendations.

Diagnostics

Once a suspicious lesion or other abnormality is detected, obtaining a correct and well-timed diagnosis is essential. AI increases diagnostic accuracy in many areas:

Radiology: Computer-aided detection (CAD) solutions using artificial intelligence techniques such as convolutional neural networks examine the facts of clinical imaging to mechanically detect, characterise and label areas of the situation such as masses, calcifications or anatomical anomalies. Radiologists can prioritise and focus on reviewing the maximum number of suspicious areas.

Pathology: Artificial intelligence methods analysing digitised histology specimens can quantify cellular styles and molecular features and categorise most cancer types, subtypes, biomarkers and various features that determine prognosis. They may meet or exceed the pathologist's guide tests.

Clinical data: Natural language processing (NLP) algorithms are implemented to extract structured information from medical notes, pathology reviews and other textual facts about affected individuals at scale. NLP uncovers critical diagnostic information hidden in unstructured documentation.

Treatment planning

With progressive screening and prognosis, the next significant challenge is finding the best treatment technique for each patient's unique cancer case. AI-based clinical selection support frameworks are emerging:

One promising piece of software involves training models and controlling the models based on historical data of treatment data, tumour characteristics, and patient features. Models can then stratify risk, predict therapeutic response, and support customised treatment plans tailored to the character.

For example, IBM Watson for Oncology analyses a patient's medical report, most cancer protocols, and professional training records to identify remedial options ranked by concordance and evidence. Such AI tools reduce the cognitive burden of synthesising exponentially evolving oncology data.

Radiation Oncology: Artificial intelligence systems are also being implemented in radiotherapy planning. By automating the labour-intensive process of shaping and segmenting tumours in 3D medical records, the AI device can optimise dose plans to maximise tumour targeting while sparing surrounding organs from radiation publicity.

Patient monitoring and care management

The road to recovery from cancer goes far beyond preliminary treatment, requiring vigilant monitoring of symptoms and tailored supportive care. Here, too, AI is presenting opportunities for more intelligent patient management:

Symptom tracking: Natural language processing methods can robotically extract vital signs, treatment effects, and outcome data from physician notes and patient-reported surveys. NLP reveals essential information in unstructured text that would otherwise require manual human evaluation.

Remote monitoring: AI algorithms can continuously process data streams from wearable sensors, IoT devices, and cellular applications to detect early warning signs of acute outcomes, such as life-threatening infections or treatment toxicity, based primarily on changes in patient behaviour and physiological patterns.

Predictive Analytics: Machine mastering has applications in predicting health care utilisation, supportive care needs, and scheduling follow-up appointments through models that address patient history, disorder progression, demographics, and psychosocial determinants.

With AI-enabled monitoring and management, providers are focused on providing better quality, evidence-based care centred on the individual patient journey.