# Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

## TL;DR

This survey explores the characteristics, key tasks, available datasets, algorithm performance, benchmarks, and collaboration readiness in the field of big medical data to guide future research and practice.

## Contribution

It provides a comprehensive overview of the current landscape, challenges, and gaps in big medical data research and practice, emphasizing interdisciplinary collaboration.

## Key findings

- Identification of unique characteristics of big medical data.
- Analysis of prioritized tasks in clinical research.
- Assessment of current algorithm performance and benchmarks.

## Abstract

Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of state-of-the-practice and state-of-the-art systems handling big medical data currently or in future? Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together? We believe answering the above issues will help define and shape the landscape of big medical data.