Decision Forests are AI algorithms that use multiple decision trees to classify data based on conditions, with the final prediction based on the collective predictions of all trees. The FDA uses Decision Forests for explanatory analysis and pattern recognition in large datasets like DNA and drug data. Training many decision trees through iterative learning allows the system to cross-validate new inputs against trained data for high accuracy. For medical applications using Decision Forests, devices and software must comply with FDA regulations by undergoing verification, validation, and potentially clinical trials before market submission. The FDA is developing frameworks to help AI-based tools avoid frequent submissions as they undergo changes by documenting early design requirements.

1. Decision Forests: FDA's Tool for Data Analysis
and Pattern-Recognition Methods
By: Govind Yatnalkar
Decision Forests (DFs) are one of the most aggressive data classifying AI algorithms. A
decision forest comprises several decision trees that perform the task of data classification based
oncertain conditions. Eachtreefunctionsasanindividual classifier or a predictor. Decision forest
combinesthe predictions by training andtesting multipletrees andin the end, thefinal prediction
is based on the collective prediction or the integrated computing power of all trees, offering an
aggressive and accurate decision-making AI-based tool.
FDA uses Decision Forests for deriving explanatory analysis with co-relations and pattern
recognition by analyzing massive datasets including DNA microarray and Structure-Activity
Relation (SAR) data.1 The DF trees are initially trained with specific sets of inputs and outputs.
For the new data, the trees predict outputs based on previously learned combination of inputs or
“patterns”. Example: Predicting an unstable drug where the trees are supplied with distinctive
chemical compounds and their behaviors as inputs.2 With such abilities, several computational
steps are eliminated, significantly improving system performance.
Training a large number of decision trees with an iterative learning process, the system
cross-validates new inputs against the trained data, contributing deeply to the overall accuracy.
Being an AI algorithm, it is indeed adaptive which means the selected AI model keeps changing
based on requirements. When embedded with a medical device or if used as an AI-based software
tool in a medical environment, the algorithm needs to comply with FDA regulations for satisfying
essential quality, safety, and efficiency standards.
Currently, applications or medical devices integrated with AI must go through some sort
ofpremarketsubmissiondepending ontheir categorizedclassandstate (Statedefines if thedevice
is substantially equivalentora new device).Surely,FDAis engaging closely with AI/MLengineers
to develop a flexible framework that would help them along with device manufacturers avoid
frequent submissions as AI-based tools undergo frequent changes. Initially, the proposed
framework defines the class of the device using the state of the healthcare and significance of the
1 FDA (November 2018) Decision Forest Novel pattern-recognition method which can be used to analyze
DNA microarray, SELDI-TOF MS, and SAR data. Retrieved on 09/20/2020 from
https://www.fda.gov/science-research/bioinformatics-tools/decision-forest.
2
Meyer, J. G., Liu, S., Miller, I. J., Coon, J. J., & Gitter, A. (2019). Learning drug functions from chemical
structures with convolutional neural networks and random forests. Journal ofchemical information and
modeling, 59(10), 4438-4449.

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information. Also, the framework states that the potential or future requirements can be
documented in early submissions. Hence, submissions would be required only if the changes fall
outside the previously documented requirements or if the changes deeply affect safety, quality,
and efficiency. The potentially documented design requirements in the early stages are called the
software pre-specifications (SPS).
Within the same framework, FDA states every AI-based software or medical device should
go through Clinical Trials or the software verification and validation phase in which the AI target
output is tested against the required functionality or software requirements.3 With the design and
requirements documented, the device or the application is ready for the premarket submission.
To sum up, Decision Forests are the best-suited tools for analyses of big data sets. Also,
they provide powerfulmechanismsforcreating andderiving data-drivenpatterns.Indeed, if used
in medical applications, they should be FDA compliant before they are released in the market. Do
you have a medical or a software device that includes Decision Forests or other AI-based
algorithms that need FDA approval? Our regulatory experts at EMMA International can help
ensure your product is compliant with the FDA regulatory requirements. Contact us at 248-987-
4497 or info@emmainternational.com for additional information.
3 FDA (January 2020) Artificial Intelligence and Machine Learning in Software as a Medical Device.
Retrieved on 09/20/2020 from https://www.fda.gov/science-research/bioinformatics-tools/decision-
forest.