Exploration of Cardiology

From: How algorithms are transforming the diagnosis of ischemic heart disease—state of the art

Table 6. Implementation challenges.

Category	Challenges	Solutions	References
Regulatory	FDA/CE approval pathways; validation standards; lifecycle oversight of adaptive algorithms	Software as a Medical Device (SaMD) frameworks; dedicated guidance; post-market performance monitoring	[49, 50, 56]
Ethical	Algorithmic bias; transparency/interpretability; liability; data privacy	Diverse and representative datasets; bias auditing; explainable AI; accountability mechanisms	[48, 51, 56]
Clinical	Workflow integration; physician acceptance; standardization; prospective validation	Training and protocols; vendor-agnostic standards; multi-center trials to demonstrate efficacy (e.g., PROTEUS)	[34–36, 40, 52]
Economic	Implementation costs, reimbursement models, and uncertain ROI	Cost-benefit analyses; value-based payment models; phased deployment; workflow efficiency gains	[36, 57]

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AI: artificial intelligence; CE: Conformité Européenne; FDA: Food and Drug Administration; ROI: return on investment.

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MT: Conceptualization, Writing—original draft, Writing—review & editing. The author read and approved the submitted version.

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