Table 1 Current potential pitfalls

1

Uneven distribution of informatics resources.

2

Integration of biomedical data located among heterogeneous sources.

3

Hazards in dehumanization of healthcare data.

4

Handling of extensively available irrelevant, error prone, and missing data.

5

Intelligent and user-friendly interface development.

6

Applying regulations and policies for data collection, usage and sharing.

7

Harmonizing big data with the definitions of clinical phenotypes and diagnosis.

8

Inflexible EHR database schemas not geared for precision medicine.

9

Lack of data availability on social determinants of health.

10

Unstandardized genomics tools and modifications in their versions and outcome format.

11

Overloaded Data generated during unnecessary follow-up diagnoses and treatments.

12

Augmented computational complexity with increasing number of attributes.

13

Slow SQL based high volume data processing speed.

14

Determining optimal parameters and understanding structures of AI and ML algorithms.

15

Handling continuous explanatory variables with more than two levels and understanding odds and probabilities in AI and ML algorithms.

16

Possibility of too many overfitting attributes in AI and ML algorithms.

17

Handling redundant attributes, distribution of statistically independent attributes, and management of class frequencies affecting accuracy.

18

Reduced evidence and reproducibility.

19

Correct predictor variables selection, and evidence-based observational data analysis and screening.

20

Gaining confidence of clinicians at AI produced results.

21

Ethical and social issues related to healthcare data collection, privacy and protection.