Computational Intelligence (CI) is one of the major pillars of Artificial Intelligence. It is the study, design, and development of intelligent software based on the theory of evolution. Within the past decade, healthcare has become expensive. Also, with the declining doctor-patient ratio, there are constant needs for computing systems for everything from executing simple tasks, such as booking appointments, to major services such as consulting and diagnosis...

1. Producing Better and Affordable Healthcare
Services Using Computational Intelligence
By: Govind Yatnalkar
Computational Intelligence (CI) is one of the major pillars of Artificial Intelligence. It is
the study, design, and development of intelligent software based on the theory of evolution.
Within the past decade, healthcare has become expensive. Also, with the declining doctor-
patient ratio, there are constant needs for computing systems for everything from executing
simple tasks, such as booking appointments, to major services such as consulting and diagnosis.
The solution to such problems is making current software systems “smart” or simply,
adding intelligence1. These are the features of CI-based applications which are helping the
healthcare and medical services become more affordable and efficient:
 Cheaper and Faster Services – Being a computerized system, CI tools can diagnose or
make recommendations in seconds. Also, these tools use minimal resources and are
mostly dependent on the data required for CI module training and testing.
 Patient Treatment Decision Making – Based on the historic patient data, unique and
customized plans for patients can be generated or recommended. Such
recommendations would directly result in saving several hours and dollars which go into
patient appointments, planning, and clinical resource management.
 Shrewd Diagnosis – Based on the patient diagnostic data, CI tools can forecast health
conditions or detect early signs of critical diseases.
 Help in Pharma and Clinical Trials- Based on the trained data of chemical compounds
and their behaviors, the CI tool might be able to predict the potential side-effects and
hazards that could affect patients in the future. Such predictions would save large sums
of money and time which might be invested to find a remedy if the designed medicine
undergoes variations or failures. Additionally, it would also save the resources to be
invested for human or animal clinical trials with evaluations for such medicines.2
CI algorithms are a part of the AI framework. To get the product on the market in the US,
it needs FDA compliance. Currently, the AI-based software undergoes a premarket submission,
such as the 51o(k) or DeNovo pathways, for getting FDA approvals. But FDA is actively engaging
with manufacturers and software developers to develop a framework that would cover the
anticipated software changes, offering flexibility as CI models include constant changes. The
1 Rakus-Andersson, E., & Jain, L. C. (2009). Computational intelligence in medical decisions making. In Recent Advances in
Decision Making(pp. 145-159). Springer, Berlin, Heidelberg.
2 Arnaud Bernaert, Emmanuel Akpakwu (May 2018) Fourways AI canmake healthcare more efficientand affordable. Retrieved on
09 /01/2020 from https://www.weforum.org/agenda/2018/05/four-ways-ai-is-bringing-down-the-cost-of-healthcare/.

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proposed Software Pre-Specifications (SPS) tool offers the space of adding potential changes
that developers see coming in future versions. In such cases, FDA states that it is not essential to
provide a submission if it is stated in the SPS.3 Indeed, if there are changes that are strongly
affecting the safety, quality, or effectiveness of the CI tool, submission is necessary.
Moreover, even if the CI tool predicts or analyses data accurately, every medical device and
application goes through the clinical evaluation phase. In the case of CI software, the outputs of
the CI are validated against the established target policies to check if the model outcome reaches
the intended use in the target population. The clinical evaluation is an essential step for
checking the overall accuracy of the implemented CI algorithm.4 This phase also involves the
feedback of the Internal Review Board (IRB) committee which checks the methodologies applied
during the research and implementation phases. IRB checks if the utilized methods are valid
and ethical.
To sum up, even though the CI tool assists in cutting down the healthcare and medical costs
while making it better, it needs to be thoroughly tested not only from an algorithmic perspective
but also from a quality and safety perspective. Do you have a CI-based software that needs FDA
approval? Our experts at EMMA International can help ensure your product is FDA compliant.
Contact us at 248-987-4497 or info@emmainternational.com for additional information.
3 FDA (January 2020) Artificial Intelligence and Machine Learning in Software asa Medical Device. Retrieved on 09/03/2020 from
https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-
m edical-device.
4 Jon Speer, Allison Komiyama,Michelle Rubin-Onur (July 2019) Regulating Artificial Intelligence and Machine Learning-based
Software asa Medical Device. Retrieved on 09/03/2020 from https://www.greenlight.guru/blog/regulating-artificial-intelligence-
m achine-learning-software-as-a-medical-device.