Are Applications Of AI To Healthcare Patentable?

Once the pitfalls are avoided, this is a healthy area for obtaining patent protection. Our experts James Varley and Tom Leanse point the way:

Artificial intelligence (AI) is increasingly being used across society for a wide range of applications, from voice and image recognition to predicting our shopping habits. Healthcare has become an area of particular focus and activity, with AI being used to search for new treatments, and to assist in the diagnoses of patients. From the perspective of European patent law, the application of AI to healthcare is particularly interesting because, at least at first glance, it seems to relate to several different types of patent exclusions: the exclusions for mathematical methods, computer programs and methods of treatment and diagnoses performed on a human body.

Article 52(2) of the European Patent Convention (EPC) lists a number of subjects that are not regarded as inventions by the European Patent Office (EPO). Among them are programs for computers and mathematical methods. On the face of it, AI would fall into both of these categories. However, Article 52(3) of the EPC goes on to state that applications related to these subjects are only excluded "to the extent to which a European patent application or European patent relates to such subject-matter or activities as such".

In practice, the EPO considers both mathematical methods and computer programs to be patentable where they "contribute to the technical character of an invention, i.e. contribute to producing a technical effect that serves a technical purpose". The most recent version of the EPO Guidelines for Examination provides a non-exhaustive list of technical purposes that may be served by a mathematical method (and by extension, a computer program). Among these are "providing a medical diagnosis by an automated system processing physiological measurements".

As an example, consider the use of AI in the paper "Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care" (Nat Med. 2018 Nov; 24(11):1716-1720). Here, a reinforcement learning algorithm is applied to 48 different bits of patient data in order to identify sepsis treatments that would maximise the patient's 90-day survival chances. The results showed that mortality rates were lowest among patients whose clinician has followed the recommendation of the algorithm.

Comparing this method to the technical purpose provided in the EPO guidelines suggests...

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