Actually I will not spam you and keep your personal data secure
Artificial Intelligence is one of the most hyped terms in technology recently. However – beyond the hype there is a lot of potential and one of the areas that are most likely to benefit from good and stable implementations of AI is Healthcare.
There are already early signs of hope in this area and there seems to be empirical evidence that data mining and AI algorithms can dig through complex medical data and recommend personalized treatments.
Among these uses I would note
However, there are many more other uses and we are just beginning to scratch the surface of what can be done. Recent advancements in the fields of deep learning and especially neural networks have been hailed and hyped and its uses in healthcare are very interesting.
However, a few challenges and recommendations are in order, given the sensitive, yet promising nature of this field.
A new report requested by the U.S. Department of Health and Human Services (HHS) was conducted by an independent advisory group of academic under the name of JASON. The report outlines a some findings and recommendations on the topic.
In terms of clinical practice the group found that recent advancements uses peer reviewed result, thus protecting from poorly validated AI implementations. It also found that implementing new procedures using AI for diagnostics will require very strong validation before being used in treatments.
The study recommends that AI research in healthcare should be thoroughly tested, especially outside its training sets.
The study shows the confluence of AI and smart devices that can lead to additional streams of data. This happens mostly independent of the health care industry, mostly under consumer tech developments.
Two aspects stand out in the report’s recommendations on the topic: monitoring data transparency and privacy and monitoring foreign health implementations and failures.
The researchers show how important clear and effective data sets are in developing new tools. However, these data sets have to be correctly labeled and extreme care should be taken in managing electronic health records and their output as data sets.
One important aspect the researchers point to is the potential usage of AI algorithms to understand disease correlations and personalizing patients with the best care possible.
Their recommendations focus on new ways of gathering, cleaning and sharing data sets with AI researchers that can make the best use of these data sets.
Although there are many issues to be solved, AI shows real promise in healthcare. As the researchers note, the general public is willing to push forward, mostly due to “1) frustration with the legacy medical system, 2) ubiquity of networked smart devices in our society, 3) acclimation to convenience and at-home services like those provided through Amazon and others. “