How can you implement AI in healthcare

One of the most critical issues in modern healthcare is reducing preventable deaths, increasing patient satisfaction, and ensuring future success. In my opinion, you will enable this through the proper use of artificial intelligence in healthcare. I am not a doctor or health professional, so I will attempt to write about what can currently be done with AI in healthcare and how it might lead to better patient care.

AI has huge potential in general medicine and surgery because:

– General medicine is already data driven (tests, symptoms), and some types of medical decisions are algorithmic (such as drug dosage).

– Surgery requires extensive data collection on performance metrics such as complications rate, mortality rate, etc. It involves both time-series data and linked clinical data.

– Almost all healthcare happens in a hospital, so many patients can be seen quickly to train the AI system. However, this also means that you have less control over what kind of patient population will be used for training or testing since it is limited to hospitalized people. 

It could lead to biased estimates when deployed outside hospitals. The bias may get amplified by nonlinear relationships between features and outcomes. Here I describe how doctors might collaborate with AI systems in the future.

Ways artificial intelligence can help – Michael Osland  

There are two ways that artificial intelligence can improve care: detecting errors early and suggesting treatment options well ahead of time (for example, by spotting correlations in the input data without requesting human intervention). Detection of errors early is crucial because it can prevent a lot of damage to health and money.

The first kind of error that AI systems can identify is a misdiagnosis, which means that the correct disease was not recognized. Sometimes these mistakes are down to simple human error. But they also happen for many other reasons: doctors may have difficulties diagnosing rare diseases, distinguishing between similar-looking symptoms, or they might not have all the information about a patient available. 

This problem will become less pressing as more data becomes available from wearable devices etc says Michael Osland. Also, patients often know their condition better than any doctor since they keep detailed diaries on how they feel and what treatments work for them. So I think that symbiosis between “AI-doctor” and patient could be very effective in the future.

The second kind of error is when a treatment or surgery goes wrong. This can be directly detected by imaging machines such as MRI. However, these errors are hard to spot without specialized software because they involve many different factors: disease, age, gender, genes (FUT2 gene G and A alleles), environment (smoking, drinking). I think AI systems will become more accurate at detecting misdiagnoses and mistakes in general medicine by using data from electronic health records (EHR) combined with time-series measurements from wearable devices/smart homes.

AI makes things easier 

AI systems will find it easier to suggest new treatments make diagnoses because the former often involve many linear relationships (symptoms vs. drugs). In contrast, the latter involves highly nonlinear relationships. Another reason is that general medicine is already based on evidence from randomized controlled trials (RCTs) and clinical practice guidelines. 

For diagnosis, AI might be able to predict rare diseases by cross-referencing symptoms in a database rather than building a model like current research projects in data mining does. These algorithms could become even more powerful if we make them use temporal information such as whether symptoms appeared before treatment or after diagnosis etc.

The significant advantage of using AI systems in healthcare would be producing better care for patients at lower costs. It can happen through early detection of errors or new treatments not currently available.