ML in medicine: opportunities and threats 

AI in medicine threats

Artificial intelligence in medicine is an area that is constantly evolving and is on an upward trend. Precise analysis of human anatomical structures or automatic localization of inflammation or cancer are some of the many examples of symbiosis between man and machine. In medicine, artificial intelligence is used primarily in imaging diagnostics such as MRI (resonance) and CT (computed tomography). 

The development of predictive analytics in healthcare using machine learning tools and techniques in medical algorithms has made it possible to detect health problems early. They significantly facilitate the work of many scientists, but also doctors, supporting their decision-making processes in their daily clinical work.

Predictive analytics in healthcare using machine learning tools and techniques: image recognition

Image recognition technologies are based on the fact that algorithms first learn what a healthy image of an organ looks like. They then detect potential lesions or cancer. With the right amount of database to train, these algorithms can effectively recognize anomalies and mark places on a given organ that require further diagnostics. This process is called segmentation or area of interest identification. 

AI plays a vital role in image processing in a variety of forms, including machine learning, deep learning, and neural networks, changing the way medical imaging is done from image detection to initial or targeted diagnosis. Various machine learning and deep learning algorithms have been developed to help the radiologist automate some of his or her work processes. If this topic is interesting to you, we recommend you to read our case study about custom automated and semi-automated algorithms to liver and liver tumor segmentation and analysis. 

AI in the daily work of medics: survey conducted on patients in the US

A survey conducted by the Pew Research Center explored the public’s views on artificial intelligence in health and medicine — an area where Americans may increasingly encounter technologies that perform things like screening for skin cancer and even monitoring a patient’s vital signs. 

The survey found that respondents feel significant discomfort with the idea of using AI in their healthcare. Six in ten U.S. adults say they would be uncomfortable if their healthcare provider only relied on artificial intelligence to diagnose illnesses and recommend treatment. Only 39% of those surveyed said they would feel comfortable doing so. 

The results of this survey, although not fully ideal for artificial intelligence, raise some hopes. The acceptance rate will likely increase year by year. The more accurate solutions are used, the greater the public trust. 

How patients would feel if their healthcare provider relied on AI in their medical care

Source: Own study based on pewresearch.org [1]

AI for patient comfort: what are the prognosis?

The world is facing many challenges in the world of medicine. One of them is staff shortages and thus rising costs of services. The WHO forecasts that by 2030 there will be a shortage of up to 9.9 million doctors, nurses, and midwives. 

Although AI can be a panacea for the indicated problems, the process of mass implementation of AI in hospitals and clinics is complicated. The key to the sustainable introduction of artificial intelligence to medical entities is to ensure maximum safety for all elements of the chain.  

Misinterpretation of patient results by artificial intelligence could lead to a real threat to life and difficulties in determining the issue of responsibility. After all, the algorithm is not able to bear the consequences of a misdiagnosis. And unfortunately, there is no shortage of them. In the USA, as many as 250-440 thousand people lose their lives every year as a result of medical errors. What’s worth highlighting is that another branch of ML solutions, which is predictive analytics in healthcare using machine learning tools and techniques also need a lot of improvements. 

Potential problems in AI development: data

The Massachusetts Institute of Technology has created a study in which it points to humans as the main problem in working on artificial intelligence. To be more precise, it is about the potential defectiveness of the data we provide. In the journal Patterns, Marzyeh Ghassemi, who is an assistant professor at MIT and has been conducting research on the use of technology in healthcare for many years, wrote in an article published on January 14, 2022, that if used carefully, artificial intelligence could improve efficiency in healthcare and potentially reduce inequalities. As a result, the AI model can generate worse outcomes for less represented groups. 

One of the challenges may be the imbalance of data in terms of gender or skin color. If the information is collected mainly from men, the algorithm may lose accuracy in modelling similar cases in a group of women. On the other hand, an algorithm that “learns” on people with fair skin will perform worse in the diagnosis of dark-skinned people. Non-objective results are a very high threat, which can lead to misguided reconnaissance and improper determination of the therapeutic process. 

Humans are the main problem in working on AI

Developing accurate and efficient segmentation algorithms for medical images remains a challenging task, mainly due to the limited availability of publicly accessible datasets with high-quality annotations. However, due to the significant interest in this field, there is a growing trend of data sharing to further accelerate the development of algorithms for medical imaging analysis. A recent dataset “Sparsely Annotated Region and Organ Segmentation (SAROS)” published in Nature is a prime example of this. It includes 13 semantic body region labels and 6 body part labels on top of 900 CTs from the The Cancer Imaging Archive (TCIA) to maximize reusability for other research groups. 

Summary: what’s the point

Finally, it is worth asking yourself: Does machine learning allow for early detection of health problems? In our opinion, as experts creating medical algorithms, the answer is definitely yes. Despite the challenges that face the correct training of the algorithm. We are convinced we shouldn’t be scared of AI in our daily lives according to healthcare. 

All research on the development of artificial intelligence has one common denominator. Namely, it is about access to large training data sets. Artificial intelligence tools train themselves by processing and analyzing huge amounts of data. These, however, are created by people who are fallible and whose judgments can be clouded. An additional problem is also the fact that many institutions do not want to share them. 

To sum up, machine learning techniques can be effective as long as the quality, quantity, and representativeness of the data used for training modelling allow. Failure to complete all these functions translates into the possible bias of AI algorithms and, as a result, a high risk of generating much worse quality results. That is why it is so important to ensure the appropriate and good quality of the data used to train the algorithm.