, (2023) identifies lack of data quality and bias, lack of transparency and interpretability

The gap

Evidence profile

Sourced from the discussion and future work and recommendations of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 2 journals. Those papers have been cited 22 times in total.

Supporting evidence — 3 representative gaps

• Role of Artificial Intelligence in Spirituality Among Palliative Care Patients: An Integrative Review (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  , (2023) identifies lack of data quality and bias, lack of transparency and interpretability in AI algorithms privacy concerns, legal and ethical issues, cultural sensitivity, emotional and psychological impact, resistance from healthcare professionals, overreliance on technology (Utaria-Munive, 2024), unintended consequences and algorithm bias, patient trust and perception and integration into clinical workforce pose a significant challenge as well.

  generaldiscussion
  Keywords: lack bias identifies quality transparency interpretability algorithms privacy concerns legal ethical issues cultural sensitivity emotional
• The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare (2025) · Frontiers in Digital Health · cited 18× · doi

  such as adaptive AI models and improved data security protocols. The should remain with leadership of multidisciplinary physicians, who must balance technological efficiency with human empathy to keep healthcare patient-centered. should explore mitigation strategies, teams The future of AI in medicine requires collaboration between healthcare providers, data scientists, and policymakers, ensuring literacy. Future ethical deployment research should focus on integrating AI into primary care, refining regulatory models, and ensuring AI-driven healthcare remains inclusive. and enhancing digital Ultimately, medicine is not just about technology but a human- centered transformation. Ensuring AI supports patient-centered care demands a steadfast commitment to equity, privacy, and ethical governance. Future research should prioritize responsible AI deployment, accessibility, and regulatory frameworks that promote fairness and transparency across global healthcare systems. original draft, Writing – review & editing. IN: Conceptualization,

  generalfuture work
  Keywords: healthcare centered future ensuring models human patient medicine ethical deployment care regulatory adaptive improved security
• “Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites (2025) · Frontiers in Digital Health · cited 4× · doi

  (cid:129) Public trust can be undermined by exaggerated promises and (cid:129) Promotes the need for transparent, validated communication about opaque practices. datasets and technology. (cid:129) Excessive trust in AI-based tools can lead to blind faith in flawed (cid:129) Advocates for avoiding the pitfalls of stealth research (e.g., systems, while insufficient trust may hinder adoption. (cid:129) Transparency is vital for building digital trust, aligning with frameworks such as the World Economic Forum’s Digital Trust Framework (cybersecurity, safety, transparency, etc.). Theranos). (cid:129) Recommends adopting established guidelines for transparency and public trust until regulations are formalized. that patterns A, B, and C could negatively affect public trust in voice AI health-tech while this field is still new and holds much potential. 5.1 Pattern A and B: strategic marketing in digital health and unpredictable healthcare reconfigurations 5.1.1 Elements contextualizing patterns A and B 5.1.1.1 Digitalization of healthcare systems The start-ups identified for the study are all based in Organisation for Economic Cooperation and Development (OECD) member or key partner countries, whose healthcare systems face a similar set of complex and pressing issues: aging populations, shortages of healthcare professionals, and unsustainably high costs for both patients and institutions, among others (16). Digitalization is seen as a way to use technology to solve some of these issues, and healthcare systems in OECD countries have digitalized to varying degrees over the past decades (17). Large and small actors (including start-ups) in the private sector are most often the innovators that leverage technology to produce and market tools intended to address the deficiencies of modern digital healthcare systems. 5.1.1.2 Voice AI health-tech as an emerging market within digital health Digital health can be defined as knowledge and practice associated with the development and use of digital technologies to improve health, encompassing subfields such as telemedicine or telehealth, mHealth, and algorithmic medicine (18, 19). The products being marketed by the voice AI health-tech start-ups in our sample fit this definition and into these sub-fields, as they use mobile phones (mHealth) to diagnose or monitor diseases or conditions remotely (telemedicine) using AI (algorithmic medicine). Digital health is a burgeoning market that has grown exponentially in the past decade, initiatives promoting it to transform healthcare (20). Digital health was valued at $240.9 billion in 2023 and projected to grow at a compound annual growth of 21.9% from 2024 to 2030 (21). Start-ups have become key players in this sector, leveraging the unprecedented technological advancements of the last decade to produce digital health solutions that aim to shift the way we provide care (22). largely because of government Voice AI health-tech is a new field within the digital health sector, and the sampled start-ups face the challenge of creating a

  generalrecommendations
  Keywords: health digital trust healthcare systems start voice tech public technology transparency sector market based tools

Questions about this gap