Journal of Digital Health

Innovation management in AI advancement: Revolutionizing health system and biopharma

Sabeen Khaliq — 2025-07-22

The integration of advanced technologies is reshaping the healthcare and biopharma industries and requires effective innovation management frameworks to leverage their potential while fully addressing associated risks. This literature review explores the global landscape of AI-driven innovation in the healthcare and biopharma, examining strategies, challenges and future directions. Technological applications are revolutionizing the pharmaceutical value chain by accelerating drug discovery and development, optimizing manufacturing processes and enhancing patient care delivery. For instance, AI-assisted drug development has reduced the time for candidate identification from years to just a few months, as demonstrated by the DSP-1181 project. Healthcare organizations employ statistical and symbolic methods to improve operational efficiency and patient outcomes. Despite promising advancements, significant challenges persist, including regulatory approval complexities, transparency and validation of algorithms, intellectual property protection, and ethical concerns such as data accuracy and bias. The responsibility for autonomous systems introduces additional difficulties in ownership and accountability. Successful adoption requires strategic planning for technology deployment alongside compliance with evolving regulatory and international standards. This review synthesizes current trends while identifying gaps in implementation. It provides structured insights for researchers, practitioners and policymakers.

Enhancing patient autonomy in data ownership: privacy models and consent frameworks for healthcare

Minal R. Narkhede — 2025-03-03

Patient autonomy in healthcare has become increasingly significant in the digital age as individuals seek greater control over their health data. This review examines the ethical, legal and technological aspects of patient data ownership, emphasizing the need for privacy models and consent frameworks to empower patients, safeguard privacy and enhance transparency. Traditional doctor-patient confidentiality faces challenges due to advancements such as electronic health records, artificial intelligence and wearable technologies, necessitating updated frameworks to protect patient rights. Privacy models such as private, public and hybrid models present varying implications for data control, security and societal benefits. Emerging technologies such as blockchain and AI are revolutionizing data privacy by decentralizing data storage and enabling patient control while ensuring secure and ethical data utilization. Advanced consent frameworks, including dynamic and granular consent, provide patients with flexibility and transparency and promote trust and active participation in data-sharing decisions. Real-world implementations, such as Australia’s My Health Record and Estonia’s e-Health system, demonstrate the potential of patient-centric privacy frameworks to enhance healthcare quality and innovation. However, significant challenges persist, including regulatory ambiguities, cybersecurity risks and gaps in digital literacy. Addressing these issues requires collaboration among stakeholders to develop adaptable, secure and interoperable systems that prioritize patient autonomy. By integrating patient education, fostering interoperability and leveraging adaptive technologies, healthcare systems can balance privacy and innovation, build trust and ensure ethical data practices that empower individuals while advancing public health objectives.

MedBrain® for making diagnosis of childhood illnesses: a case report

George Uchenna Eleje — 2025-05-09

Diagnosing childhood diseases can be particularly challenging, especially in areas with limited access to specialists. This case report explored the application of MedBrain^®, an artificial intelligence–powered Clinical Decision Support System (CDSS), in supporting diagnosis in paediatric practice. We presented two clinical cases from Nigeria: a 4-year-old girl diagnosed with urinary tract infection and a 3-year-old girl diagnosed with acute gastroenteritis. These cases were selected to demonstrate common but diagnostically challenging paediatric conditions with overlapping symptoms. For each case, MedBrain^®was used prospectively to generate a ranked list of differential diagnoses with corresponding diagnostic confidence scores based on patient symptoms and clinical findings. The system utilized a hybrid algorithm combining rules-based logic and machine learning to assess input data and compute likelihood estimates. In both cases, MedBrain^®'s top-ranked diagnoses— urinary tract infection (96%) and gastroenteritis (95%)—were confirmed by attending paediatricians, validating its clinical utility. Comparatively, the standard clinical diagnosis was initially uncertain in both scenarios due to nonspecific presentations. These findings emphasized the potential of MedBrain^® to augment paediatric diagnostic accuracy, particularly in low-resource or non-specialized settings. Future studies should evaluate MedBrain^® in larger prospective cohorts or randomized control trials and compare its diagnostic performance with established clinical guidelines.