Table 2.

Challenges involved with Machine Learning in Healthcare

ML in healthcare challenges	Description
Safety challenges	Model’s prediction precision without expert intervention is questioned Identifying rare, underlying health problems is challenging Enabling ML techniques to identify subtly hidden cases is the key to ensuring safety
Privacy challenges	Preserving privacy can be challenging Patients expect their confidential information to be safeguarded Anonymization can prevent unauthorized access and privacy breach
Ethical challenges	Data accumulation requires authorization Preserving patients’ dignity while collecting data is to be taken care of If ethical concerns are not addressed, the unfavourable impact is seen in ML applications
Availability of quality data	The information available is heterogenous Data collected during practice have issues (bias, redundancy), produce an adverse effect in the algorithms High-quality practical data requires resources and service with good maintenance
Casualty is Challenging	Reasoning while taking decisions in crucial health problems is imminent Queries where expert reasoning is required cannot be answered from a medical data perspective Forming casual rationalization from data is challenging
Updating hospital Infrastructure is inflexible	Independent sections of healthcare avoid frequent information exchange For frictionless communication, antiquated systems need upgradation The difficulties in upgrading hospital infrastructure raise concern with modern-day healthcare practices using ML/DL