The Role of AI in Early Cancer Diagnosis And Treatment

The Role of AI in Early Cancer Diagnosis And Treatment

AI in Healthcare

Updated on 07 Aug 2023

Written by Dr. George Laliotis

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As we move further into the digital age, the role of artificial intelligence in various fields continues to expand. One of the areas where AI has made a significant impact is healthcare. Specifically, AI in cancer diagnosis is changing the way medical professionals and researchers approach the detection and treatment of this pervasive disease. 

In this article we will discuss the huge impact and potential of AI in oncology, highlighting breakthroughs, challenges, and future possibilities.

The Role of AI in Cancer Diagnosis

The role of AI in cancer diagnosis and treatment has been underlined by various promising developments around the world. A collaboration between the Mass General Cancer Center and MIT has yielded an AI tool named Sybil. This tool demonstrates its prowess by anticipating the development of lung cancer as much as a year in advance with an astounding accuracy rate between 86% and 94%.

Sybil scrutinizes CT scans in a manner distinct from human radiologists, spotting early indicators of a disease that might otherwise remain unseen. Although it has not received FDA approval for non-trial usage, it holds the promise of a transformative approach to early lung cancer detection, thereby improving survival rates. Nonetheless, this does not negate concerns about AI training, data diversity, and the potential for overdiagnosis.

Meanwhile, the Royal Marsden NHS Foundation Trust, the Institute of Cancer Research, and Imperial College London have all collaborated to create a new AI cancer detection model that can recognise cancerous growth in CT scans.

The AI uses radio mics, a method for extracting important information from medical photos that the human eye frequently misses. It outperformed currently available clinical diagnostics by demonstrating an accuracy rate of 87% in identifying malignant lung nodules. Although additional testing is required before this approach can be included in healthcare systems, it promises to speed up early cancer identification and treatment.

AI in cancer detection is further highlighted in Hungary. Here, it’s spotting signs of breast cancer that human radiologists might overlook, raising questions about AI's potential to replace human roles in medicine. Using AI tools, the radiologist Dr. Éva Ambrózay detected potentially cancerous areas on a patient’s mammogram that two other radiologists had previously missed.

Current advancements in AI show a remarkable ability to detect cancer at least as proficiently as human radiologists, marking a significant leap in public health. However, numerous hurdles stand in the way of widespread adoption, including further clinical trials, ensuring accuracy across diverse patient demographics, and reducing false positives. Despite these challenges, many experts see AI as an invaluable partner for trained doctors, rather than their replacement.

Docus.ai represents another advancement in the realm of AI-enhanced healthcare. As an AI health chatbot, it provides a valuable service by analyzing complex medical data. With its ability to make different diagnoses and provide disease likelihoods based on a patient's medical history and current symptoms, Docus.ai stands alongside the significant innovations previously mentioned.

It's not merely about processing data, but the platform's skill in recommending important tests, generating detailed health reports, and its intuitive communication with users using natural language processing adds to its diagnostic capabilities.

Having explored the impact of artificial intelligence in cancer diagnosis, let's delve into the specific mechanisms through which AI achieves this.

AI cancer detection and diagnosis

How does AI Detect Cancer?

The transformative role of ML and AI in cancer diagnosis cannot be overstated. The trajectory of FDA-approved AI-powered medical instruments, commencing with a device for cervical slide interpretation in 1995, has soared to a staggering 521 approvals by May 2023.

A considerable number of these devices are being used for early cancer detection, thereby elevating the likelihood of positive patient outcomes, which encompass possibilities of cure, diminished necessity for systemic therapy, and preservation of quality of life post-treatment.

AI plays a significant role in analyzing large databases to identify patients at increased risk of cancer or displaying early signs. Leveraging pattern recognition and natural language processing, AI navigates through an intricate web of electronic medical records, identifying patients with particular signs, symptoms, or known risk factors linked to specific cancer types.

This methodology proves instrumental in unearthing risks associated with relatively infrequent but lethal cancers, such as pancreatic cancer, thereby amplifying the efficiency and cost-effectiveness of population-wide screening.

Image analysis is another area where AI and cancer detection intersect. Contrary to human vision, machines are impervious to fatigue, distraction, blind spots, or the phenomenon known as "inattentional blindness." A case in point is a study where a deep learning algorithm surpassed radiologists in identifying lung cancer on chest X-rays.

AI also finds use in the histologic analysis of specimens, assisting in real-time analysis. AI's use has shown tremendous success in cancer diagnosis with significant accuracy, determining cancer grades such as the season score for prostate cancer, and detecting lymph node metastasis.

It also shows great potential in predicting gene mutations, which can speed up analysis and cut costs.

Emerging fields combining extensive data analysis with pathology assessment and image analysis, along with blood-based multicancer detection tests, are also using AI algorithms.

It's essential to remember that AI is not replacing doctors but is aimed at enhancing their performance and increasing accuracy and efficiency. As AI continues to shape cancer diagnosis, understanding the intricacies of human-machine interaction is becoming increasingly critical for optimal outcomes.

The Role of AI in Cancer Treatment

AI is progressively transforming the landscape of cancer care, playing a pivotal role in areas from diagnostics to treatment adherence. In diagnostics, machine learning and deep learning algorithms enhance efficiency and accuracy in interpreting mammograms and streamline processes in radiation oncology.

AI in cancer diagnosis aids in reducing redundancies for clinicians, making early diagnosis and treatment more accurate. An offshoot of AI and radio mics combines clinicogenomic information to characterize tumors and predict treatment responses and adverse effects. It automates the labor-intensive task of tumor annotation, enhancing clinicians' productivity and potentially mitigating burnout.

AI's potential extends beyond diagnostics. Penn Medicine has harnessed a 'Penny' AI chatbot to improve cancer treatment. Artificial intelligence in cancer treatment is realized as Penny is targeted at patients self-administering oral chemotherapies at home.

Penny was designed to tackle the challenges of complex medication schedules and incorrect dosing. Using bi-directional texting, Penny guided patients through chemotherapy regimens, set medication reminders, and conducted weekly symptom assessments.

Penny's pilot study yielded promising results, with significant improvements in medication adherence and patient engagement in symptom assessment. Furthermore, Penny's role was appreciated by patients for its additional layer of support, increasing their confidence in medication compliance and care team interactions.

This project showcased the iterative and collaborative nature of AI in cancer research, leading to the development of a patient journey dashboard to provide clear scheduling and dosing guidelines.

In the administrative realm, AI assists in managing tasks to allow more patient-clinician interaction time. While there's a gap between AI research and practical application, tackling it requires regulatory changes and greater patient involvement to ensure safety, reliability, trust, and equitable health outcomes.

Despite these challenges, AI's ability to harness vast digital data points to a transformative future in cancer care, enhancing patient care and quality of life.

3 Companies in AI Cancer Diagnosis and Treatment Market

Mindpeak

Marking a milestone in computational pathology, Hamburg-based Mindpeak has received the CE-IVD Mark for BreastIHC, a groundbreaking AI software that detects and quantifies breast cancer cells, qualifying them for primary diagnosis.

This stamp of approval not only paves the way for Mindpeak's expansion across European pathology practices but also addresses the surging demand for AI in cancer diagnosis.

Being a pioneering company in securing approval for a deep learning solution capable of cellular-level distinction between tumorous and non-tumorous structures, Mindpeak has indeed raised the bar for AI cancer detection.

Breast IHC, complying with all essential health and safety standards as per EU directives, is a breakthrough that bolsters efficiency and accuracy in primary diagnosis, explains CEO Felix Faber.

The intricacy of detecting and quantifying immunohistochemically stained breast tumor cells, a task having a direct bearing on a patient's treatment decision, is now simplified with AI support, mitigating human error and improving the handling of increasing cancer case numbers.

The BreastIHC solution stands out with its easy implementation, instant detection, classification, and quantification of breast cancer cells, minus complex setup or calibration.

The innovative algorithm can differentiate between tumorous and non-tumorous structures under diverse lab conditions, thereby improving the scoring in the tumor microenvironment.

Another noteworthy feat of Mindpeak's AI solutions is their PD-L1 AI system, chosen by Ziekenhuis Netwerk Antwerpen (ZNA) and GZA Hospitals, marking its first use in Belgian labs to combat cancer.

This system, integrated into the Telemis-platform, enables quicker, more reproducible diagnosis by comprehensively examining tissue images from biopsies and resuscitates, particularly of the lung (PD-L1). "With our AI, the matching of cancer patients to drugs can be significantly improved, which in turn greatly increases the chances of cure," states Faber, emphasizing the crucial role of AI in cancer treatment.

Since 2018, Mindpeak, founded by Faber and Dr. Tobias Lang, has partnered with international labs and top pathology service providers. With an expanding product range and its commitment to digitizing the pathology workflow, Mindpeak aims to transform AI cancer treatment and enhance survival rates globally.

mindpeak

Proscia

Proscia, a trailblazer in software solutions, is championing the evolution of pathology into a data-centric sphere with a cutting-edge concentric digital pathology platform and potent AI applications.

Harnessing AI is unlocking insights that fuel discovery, amplify patient outcomes, and bring precision care to fruition. Their platform finds use in labs across the globe, including reference labs, academic medical centers, CROs, and leading pharmaceutical organizations, supporting over 6,000 pathologists and researchers daily.

Concentric platforms, a sturdy and scalable digital innovation, merge seamlessly with the technology ecosystem of pathology labs. It serves as the backbone for AI applications, pushing the digital evolution of laboratories. Concentric binds together the entire pathology ecosystem, harmonizing with all technology, offering flexibility, and securing investment.

The platform is adept at collaborating with all premier WSI scanners and centralizing all pathology data in a single accessible hub thanks to its open API. Any AI application can be launched from their platform, invigorating computational pathology in the organization.

Furthermore, Concentric brings forth powerful image viewing and analysis, promotes sharing, collaboration, and remote consultation, and enables lab-defined workflow management. Their robust data management can be either on-premises or cloud-based.

At Proscia, they provide Concentriq Dx and Concentriq for Research. Each is intentionally designed to meet the specific needs of diagnosis and research environments.

Proscia's roots go back to Johns Hopkins University where it was founded in 2014, with its headquarters now located in Philadelphia. The company takes pride in partnering with six leading academic research centers and having over 10,000 pathologists and scientists employ Concentric.

proscia

Xilis

Xilis is spearheading advancements in precision oncology and drug development, setting sights on a future where cancer treatment is significantly revolutionized.

Their proprietary MicroOrganoSphere (MOS) technology is engineered to improve the efficiency of AI in cancer diagnosis and treatment, presenting an ideal solution where only a fraction of patients currently benefit from effective treatment.

MOS technologies create microscale tumors that encapsulate the unique structure, genetic alterations, gene expression, immune microenvironment, and histopathology of individual patients. This breakthrough empowers clinicians to make personalized therapy decisions that are both precise and timely.

Their Precision Oncology Platform enhances the role of AI in cancer treatment by enabling clinicians to make crucial, time-sensitive treatment decisions.

The Platform predicts a patient's response and suggests optimal therapeutic options using the Xilis Response Score, a novel tool devised to guide treatment decisions with priority.

In parallel, Xilis's MOS technology is reshaping the landscape of cancer drug development. They offer a comprehensive patient-centric solution that not only encompasses high-throughput drug candidate screening and pre-clinical assessment of immuno-oncology drugs but also includes personalized assays for determining clinical trial eligibility and companion diagnostics to steer patients toward new treatment avenues.

MOS technology is an efficient, scalable, and consistent system that mirrors the heterogeneity of a patient’s tumor, thereby effectively capturing the complexity and diversity of primary tissue.

This puts MOS in the position of being a predictive tumor model apt for measuring cell viability, drug response, and critically, for prioritizing patient care. Their machine learning tools augment this process, enabling AI cancer detection through rapid diagnosis from biopsies and high-throughput screening for drug discovery.

The culmination of these efforts is a precision oncology platform that not only revolutionizes the way we approach AI cancer treatment but also catalyzes the pace of drug development, all while keeping patient care at the forefront.

xilis

The Future of AI in Cancer Diagnosis and Treatment

The potential for artificial intelligence in cancer care extends beyond prevention and diagnosis to treatment and daily life. However, technology's path through a hype cycle – from Trigger to Productivity phases – often hampers the realization of its full utility.

Overcoming these stages requires cooperation between research, medical, governmental, and community implementation experts, shifting the focus from high-incidence cancers to less prevalent ones.

The fusion of AI with other digital technologies, such as telemedicine and the Internet of Medical Things (IoMT), stands to significantly enhance medical practices. It does so by identifying causative relationships between variables, thereby refining risk stratification and informing treatment decisions.

Further clarity will be provided by 'Explainable AI' and 'Interpretable DL', which shed light on model functioning, potential biases, and decision-making processes. The key to unlocking these potentials depends on collaboration between biologists and computer scientists, as demonstrated by initiatives like Cancer Moonshot.

Customized cancer treatment, the direction of future oncology, will be powered by living databases that capture every health aspect of an individual. This comprehensive data can drive complex models to customize therapy selection, dose calculation, and surveillance. Bayesian Model Averaging (BMA), a promising tool for precision medicine, allows for considerations like comorbidities and drug interactions.

BMA uses tools that look like those that scientists could use in the lab and communicates in a manner that biologists can understand. Don't forget about the self-diagnostic AI cancer detection mobile apps like SkinVision, which were designed to help users check for cancer-in-skin anomalies, offering around 95% accuracy.

Futuristic research is delving into cellular computing. The complexity lies in understanding a cell's computational function. The integration of AI into this field will enable the installation of molecular computers within cells. This can detect diseases and trigger anti-disease responses, thus minimizing healthy cell destruction and opening up new therapeutic possibilities.

Despite skepticism, CAR-T cell therapies are successfully treating B-cell malignancies. The specificity, safety, and amplification of these therapies are further enhanced by AI integration. Additionally, CRISPR, a gene-editing tool that works in harmony with cell therapies, offers potential permanent cures.

However, challenges like DNA mutation induction persist. Fortunately, machine learning aids in predicting such CRISPR-induced changes, enhancing both research safety and efficiency.

RNA therapies, another advancement, provide new tools for hard-to-treat targets. The coupling of these therapies with AI opens doors for innovative cancer treatment methods involving neoantigens and the microbiome. Further, AI-enabled sequencing improves disease monitoring, fostering a more personalized approach.

To expand access to such precision medicine, companies like Syapse, Flatiron, and IBM Watson are leveraging AI's capabilities.

Liquid biopsies, a less invasive means for monitoring disease progression, coupled with advancements in wearables and point-of-care diagnostics, are expanding data sources.

AI integration is addressing the challenges of rule-based systems, such as high development costs and complex encoding issues. It's a significant stride towards a more informed and personalized AI application in oncology.

Conclusion

AI in cancer diagnosis as well as the use of AI in cancer treatment talk about the profound capabilities of technology in the realm of healthcare. Harnessing the power of AI in cancer diagnosis, from analyzing extensive data to extracting crucial information from images, has undeniably revolutionized the field.

However, the journey toward fully integrating AI into daily clinical practice comes with its own challenges and hurdles that need to be overcome.

As we continue to refine these technological tools, the future of cancer care appears brighter, promising a more personalized, efficient, and effective approach to treating this multifaceted disease.

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