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Diagnosis is critical in treatment of human diseases and the same applies to #breastcancer among women. An accurate diagnosis can detect breast cancer leading to better health outcomes. Women have been affected by breast cancer epidemics and with 2M cases each year, cancer is becoming a serious health problem.

Screening programs for breast cancer have helped in the short-term as global health declines in addition to awareness programs to sensitize the public. Accordingly, more needs to be done from adopting health care technologies, to advanced mammography for breast cancer patient care.

Combining health care technology with breast cancer screening is critical to achieving this mission. For instance, the Contrast-Enhanced Spectral Mammography (CESM) is a breast cancer tool that provides more accuracy by 8% compared to traditional screening methods.

CESM has high accuracy compared to mammography with new tools such as digital mammography coming out. Based on 2D and 3D versions, digital mammography detects breast cancer faster and with high accuracy levels.

Despite health experts examining cancer cells via traditional methods, challenges still persist with respect to accuracy. A physician should have 100% results for scans conducted such as mammograms to advance better patient care.

Artificial intelligence promises to close this gap through accurate examinations of breast cancer.


I have attended many #AI health technology conferences in the United States, Europe and Asia, where teams showcase innovations for cancer treatments and learned so much from these experiences. Many of these solutions revolve around cancer diagnosis using technology and suppressing growth cells.

This led me to write this article given the enormous implications of cancer particularly on women, and how technology such as AI can achieve this breakthrough. Let us begin!

Breast Cancer Research and Treatment by AI

#Artificialintelligence leads the way in health technology including cancer treatment.

The first case study, I will explore is Google Health. Researchers at Google Health have developed breast cancer detection models as AI commands high accuracy compared to humans. By training AI models, researchers conducted sample scans among women and generated accurate results. Further, the experiment found that using AI to conduct breast cancer scans reduces error occurrence for all test samples.

A comparative evaluation of AI results and those of radiographers revealed similar accuracy levels and goes to show the role of AI in health care revolution. For instance, when women go for mammograms, they expect scans to be accurate to make the right health choices.

What happens when tests generate a false positive or false negative? This not only creates doubts among patients but also puts the professionalism of the physician to question. AI has come at the right time and health professionals should adopt #artificialintelligence to reduce errors and achieve optimal results.

Cancer unlike many diseases requires early management to improve patient health and according to Google Health researchers, using AI technology improves the diagnosis rate. Accordingly, the rate of diagnosis missed reduced by 12% after using AI technology and illustrates the need for adoption by health professionals.

The labor required to conduct breast cancer scans is another highlight of the Google Health experiment in which researchers compared results from the computers with those of humans with computers showing high accuracy levels. Human scan readers according to the analysis diagnose cancer at a slow rate compared to computers with the difference estimated at 90%.

From these examples, machine learning and AI technologies are needed in transforming healthcare such as breast cancer treatment. Big data is an important area as health professionals can analyze the data to offer patient solutions. Cases of patient errors during treatment abound and using big data technology is a step in the right direction. The Institute of Cancer Research in the UK combines AI and big data to generate positive outcomes in patient care.

By categorizing breast cancer types in different sections, the researchers accurately identified each conditions by use of AI. Patients need personalized medical solutions and the research from ICR demonstrates the importance of integrating technology in health care. The example of Angelina Jolie who got mastectomies highlights the importance of further research on integrating health care with technology as the future beckons.

Start-ups revolutionizing Healthcare using AI

Healthcare start-ups including Kheiron are adopting machine learning technologies in developing tools needed by radiologists in cancer treatment. AI adoption in breast cancer treatment is also happening at a start-up called BERG Health committed to patient solutions based on its #AI database and using clinical trials to examine patient health conditions . According to BERG Health, using AI has enabled the start-up to advance cancer treatment by offering accurate diagnosis and helping patients manage this condition at the early stages.

IBM Watson for Oncology is also working on cancer treatment by incorporating artificial intelligence tools in its patient management database. For example, IBM Watson develops predictive models that work by using patient medical information to a high degree of accuracy.

PAIGE is a start-up based in New York working on cancer treatment by using machine learning technologies. Cancer treatment professionals benefit from diagnostic tools from this start-up considering the challenge of detecting the disease. Thanks to machine learning tools, health professionals can use the information generated by their algorithms to manage conditions of cancer patients. The start-up has also made forays in developing an AI computer specifically for cancer treatment.

PathAI and Tempus are additional start-ups working in the cancer treatment space where they develop ML tools for detecting and diagnosing breast cancer through precision medicine. The last start-up making progress in the cancer space is IBEX which digitizes diagnosis of cancer by automating procedures. Their cancer diagnosis system is worth noting because of its ability to identify diagnoses.

Institutional based Research

MIT is working on mutiple cancer treatment research projects in which computer programs using AI are being used for scanning cancer cells. According to MIT researchers, breast cancer is among the risk areas affecting women and hence their current investment in this area. For example, the machine learning algorithms used at MIT identify breast cancer cells at their early stages and this research enables patients to adopt intervention measures.

At the same time, an AI database for cancer treatment offers real time and accurate results on patient conditions. By comparing different prototypes of patient scans, the computer systems at MIT offer accurate results within a short time span.

The New York University has developed cancer detection systems that identify symptoms of breast cancer. For instance, the researchers found a way of using AI to explore the development stages of cancer by offering the required diagnosis to patients.

By training their algorithms on patient records, NYU researchers have developed high accuracy levels in breast cancer treatment followed by reduced rates of missed diagnoses. The researchers at NYU observed that AI tools had the best accuracy levels in diagnosis of cancer and illustrates the essence of this technology in health care.

AI research at Columbia University has additionally created new innovations in breast cancer treatment. As an illustration, researchers at Columbia developed algorithms that conduct multiple scans each minute in patient databases hence reducing time required by humans in performing the same task.

From this innovation at Columbia, cancer treatment works best when health professionals integrate AI and machine learning tools. A strong computing platform like the one at Columbia University is another consideration for health organizations committed to fighting breast cancer and meeting health care standards.

Job Automation: Augmenting Researchers and Doctors

The era of manual labor in health care is over and with AI technologies being used for augmentation, automation is the next frontier of healthcare revolution. Doctors can now focus more on diagnosis and prescriptive medicine rather than manual labor as automation makes health care delivery easier.

Health care services are multi-faceted and using technology to drive quality delivery is critical in addressing diseases such as breast cancer. An example in this area is radiology where experts use AI and machine learning tools to make better decisions based on transmitted data.

Transformation in radiology continues to gain momentum as radiologists opt for better solutions such as #AI that enables them to use real time information for decision-making. Automation has reformed healthcare as technology keeps health professionals on the move and with the right information. Overall, doctors will focus on quality improvement and not labor intensive procedures that most often derail quality standards. Mammography as used in health care focuses on computer vision that in turn provides better results

Pitfalls of this use case and technology?

Using AI in breast cancer treatment has not been smooth. Cases have emerged where AI tools report errors and sometimes false positives. This is not good considering the delicate nature of breast cancer treatment. A wrong diagnosis is unprofessional and with these errors being reported, concerns about the accuracy of AI persist. Radiologists audit AI results for breast cancer scans and see discrepancies that in turn put patient health at risk.

What next?

Developing new algorithms from AI that detect cancer cells is the first start towards addressing these pitfalls. These algorithms combine mammography and pathology images with impressive results hence can address this gap. Including data points is another technique where machine learning models can learn from patterns such as family patterns, pathology examinations and demographics. Using advanced X-ray systems and 3D mammography are additional methods gaining traction in breast cancer treatment

Future Prospects of Breast Cancer Treatment and Health Care Technology

As we conclude the discussion, here is one question you need to think about: How close are we to implementing these systems in hospitals? This year? 2025? I have been thinking about the same for quite sometime now and I can say that progress is substantial and soon these technologies will become practical in health care services.

Breast cancer poses major threats to society with women being the most affected hence the need for adoption of AI technologies in detecting and diagnosis. Universities and hospitals should support the cause by sharing their data to advance the field of mammography quicker. MIT and Columbia University are making progress in this area with new research outcomes aimed at offering patient solutions. More institutions including National Health Service in the UK are advancing research in radiology by adopting technologies such as AI for better solutions

The bottom line is that medical technology is undergoing transformation and as adoption of AI in health care continues, the health industry will be fully automated in the next 5–10 years