The Future of Cancer Care Is Artificial and It's Happening Now

What Is AI in Cancer Diagnosis?

Although there have been maelstroms of debates, controversies, and confusion regarding AI's purpose and role in our everyday lives, in some industries, AI has brought forth breakthroughs, notably in the healthcare industry, where Cancer research and diagnoses are evolving through AI technologies. Artificial Intelligence involves the use of computer algorithms that are trained through machine learning, utilizing millions of medical images, patient records, and genetic information to process vast amounts of data and make predictions or informed decisions. In Cancer care, AI systems analyze medical images, including MRIs, CT Scans, Pathology Slides, and Mammograms. Remarkably, AI achieves this elite accuracy in comparison to expert clinicians. AI can identify patterns that may be more subtle and challenging to detect by the naked eye, which allows earlier and more accurate cancer detection. The American Cancer Society (2023) reports that detecting cancer in early stages can boost the five-year survival rate by up to 90% for certain cancers.

In 2020, a seminal study published in Nature, titled "International evaluation of an AI system for breast cancer screening," illustrated the capabilities of artificial Intelligence in cancer research and how it can and is improving the accuracy in mammogram interpretations. The AI model developed by Google Health can detect breast cancer in mammograms with a fascinating finding: a 5.7% reduction in false positives and a 9.4% reduction in false negatives compared to radiologists.

False Positives: These are when mammograms incorrectly reveal the presence of Cancer when there is none. Mathematically, a 5.7% reduction in false positives illustrates that AI systems are more effective at avoiding unnecessary indications of Cancer. For patients, this alleviates additional medical examinations, anxiety, and misdiagnoses.

False Negatives: Are the inverse of false positives. This is when mammograms fail to detect Cancer when Cancer is present. Mathematically, a 9.4% reduction in false negatives shows that the AI proved effective in identifying actual cases of breast cancer, which is critical because a false cancer diagnosis can lead to the tumor worsening and spreading, delaying treatment, and overall creating more negative outcomes for patients.

Most importantly, these findings highlight that AI systems can help strengthen mammogram screenings, optimize outcomes, and provide greater health opportunities for patients, reducing false diagnoses and patient trepidation.

How AI Matches or Exceeds Human Specialists

When people think of Artificial Intelligence in terms of professions, the first word that comes to mind is "replacement"; however, that is not the case for healthcare professionals. AI is not an alternative or replacement for healthcare professionals, but rather an extension of human knowledge and capabilities, with improved outcomes. AI assists them in detecting diseases earlier, reducing errors, and improving the quality of care. Furthermore, with the use of AI, healthcare experts can put their expertise to more complex decision-making and personalized care, while AI handles all the repetitive tasks.

In traditional Pathology, a Pathologist would have to manually analyze thinly cut and sliced specimens of tissue under a microscope to identify diseases, in this case, Cancer. The traditional process of pathology required extensive expertise and experience, along with a Pathologist having to analyze intricate, detailed visuals and differentiate between normal and abnormal cell structures. A time-consuming process, and interpretations can differ depending on the pathologist, experience, and workload. Also, AI has brought scientific innovation to Dermatology, specifically in the diagnosis of skin Cancer. The Journal of the American Medical Association published a study in 2021 that found an AI model could identify skin cancer with diagnostic performance comparable to that of an experienced Dermatologist, especially for Melanoma, which, when detected early, significantly improves patient outcomes.

AI has three primary components that streamline cancer diagnoses, which are

Enhance Diagnostic Accuracy: AI algorithms are trained on thousands of images of cell structures, both normal and abnormal, to recognize subtle differences that may be challenging for a medical professional to discern.

Objective-Based Decision Making: Since medical professionals are human and prone to error, and in some cases influenced by experience, fatigue, or cognitive biases, AI provides objective assessments and consistent accuracy readings.

Accessibility: The versatility of AI allows tools to be used in remote areas and underserved regions where dermatologists are scarce, helping facilitate earlier diagnosis and treatment of skin cancer.

Lastly and most importantly, AI is impeding Cancer Misdiagnoses, which is still a global issue with not just Cancer but all patient care and medical management.

According to a research study by the World Health Organization (WHO), a leading global public health agency based in Geneva, Switzerland, approximately one in five cancers is misdiagnosed worldwide, resulting in either undetected or inadequate treatment. Misdiagnoses are one of the most prominent issues in healthcare and have severe consequences for patients and their families, leading to worse prognoses, increased healthcare costs, and immense emotional distress.

Since AI uses machine learning, medical professionals can create standardized diagnostic guidelines and frameworks for hospitals and regions, helping to reduce variability and improve reliability regardless of notoriety and institutional funding across different hospitals globally.

With AI continually improving within the medical industry, it will be an instrumental tool in aiding medical expertise, ultimately supporting earlier intervention and improved outcomes for patients worldwide.

AI's Role in Accelerating Drug Discovery

Aside from Cancer diagnoses, another pervasive issue within Cancer is developing new Cancer drugs, which is a lengthy, expensive, and high-risk process, with the cost being around 2.6 billion dollars. In fact, creating new cancer drugs can take over a decade. These stages of drug discovery illustrate the demanding, intricate, and extensive work involved in discovering and creating new compounds with therapeutic benefits for cancer treatment. Including the phases of preclinical trials to ensure new cancer treatment drugs are effective and safe for patients.

However, this is now changing, and AI is at the forefront of the revolution, accelerating the process of new drug discovery. In new drug development and creation, machine learning algorithms can predict how different molecules will interact with cancer cells, streamlining the process from millions to a concise list within a few weeks, rather than years. A great testament of AI in new drug discovery and development is the research study, an "AI-designed compound for obsessive-compulsive disorder enters human trials" by researchers at Exscientia and Sumitomo Dainippon Pharma, where researchers announced that an AI model designed a compound for obsessive-compulsive disorder titled (DSP-1181) entered human trials in less than a year. A record-smashing timeline that proves oncology can follow suit in drug discovery. Similarly, Insilico Medicine has used deep learning to identify new compounds targeting fibrosis and Cancer, demonstrating AI's versatility in drug design (Zhavoronkov et al., 2019).

Since AI can help drug discovery, AI is now used in drug re-purposing, a systematic approach that analyzes clinical and genomic data to find existing medications that could be effective against other cancers. Another significant benefit of AI in Cancer diagnoses is that it reduces costs substantially through the automation of routine tasks, rigorous testing, and data collection. AI has the potential to save the United States' Healthcare System around $200 billion to $360 billion a year yearly, according to a report published HealthcareDrive in 2023. Reducing overall cost in Cancer care and research.

Challenges and Ethical Considerations

While AI is undoubtedly the next revolution in cancer healthcare, there are still ethical issues surrounding its use in the medical industry. Just as humans can make mistakes, so can artificial intelligence. Human oversight remains essential to verify and generate information, recommendations, and ensure safe medical practices for all patients, even though AI serves as a second set of eyes in healthcare treatment. The greatest concern with the use of AI is privacy. When AI learns to find Cancer and offer information regarding treatments, it needs a broad yet comprehensive amount of patient data, such as test results, medical records, and medical images of tests done on a vast number of patients. All of this data is highly sensitive and personal.

One prominent issue with the use of AI is bias, which can happen if algorithms are trained on data sets that lack diversity. This means that if AI is trained on patients of one race, similar age, and background, including socioeconomics, it might not perform as well for people who are different. Undoubtedly, wrong diagnoses, less effective treatments, or inaccurate findings will lead to less effective results. Ultimately, makes existing health inequalities even worse. Addressing this requires deliberate efforts to create diverse and representative datasets, as well as to implement fairness checks throughout the AI development process.

Secondly, the protection of patients' personal information is one of the most significant ethical dilemmas associated with the use of AI. If hackers potentially steal sensitive information, share it with unauthorized individuals, and misuse it improperly by medical professionals, it could lead to a data breach and further complications. That's why strict rules and robust security systems are necessary to ensure patient privacy remains safe and people can trust that their information is handled responsibly.

To reduce these risks by making AI systems more transparent, accountable, and equitable. By having AI systems explain their reasoning for decisions along with doctors understanding the reasoning and explaining them to patients through two-way communication, it builds credibility and trust. It's also important to have clear rules and guidelines so AI is used fairly and doesn't harm people, especially the most vulnerable. AI has the power to improve healthcare in ways unimaginable, but AI must be used carefully. Protecting privacy, removing bias, and keeping humans involved in decisions are critical. By following strong ethical standards, AI can safely help patients and strengthen healthcare worldwide.

The Future of AI in Cancer Care

The future of AI in healthcare is more than promising, it is becoming a necessary tool to further enhance and combat cancerous illnesses. AI is already being used in hospitals all over the globe, helping medical professionals create personalized care and treatment plans catered to every patient's unique needs. AI can recommend therapies that are more likely to work for each individual. By analyzing vast amounts of data, AI can recommend therapies that are more likely to work for each individual, reducing trial-and-error approaches.

As Artificial Intelligence systems and models continue to evolve at an exponential rate, we are moving closer to a world where detecting Cancer is much easier and much earlier, often before symptoms appear, and treated with greater precision, using targeted therapies that minimize side effects. In some cases, cancer could even become a manageable chronic condition as opposed to a severe life-threatening disease. This progress gives patients more options, more hope, and better chances of recovery than ever before.