The Impact of Multimodal Large Language Models on Health Care's Future

Article Multidisciplinary Sciences

Foundation models for generalist medical artificial intelligence

Michael Moor et al.

NATURE (2023)

Article Health Care Sciences & Services

Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial

Bertalan Mesko

Summary: This paper discusses the research field of prompt engineering and its importance in healthcare. It provides practical recommendations to help healthcare professionals enhance their interactions with LLMs.

JOURNAL OF MEDICAL INTERNET RESEARCH (2023)

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Article Health Care Sciences & Services

The imperative for regulatory oversight of large language models (or generative AI) in healthcare

Bertalan Mesko et al.

Summary: The rapid advancements in AI have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has gained attention due to their diverse applications, but caution is necessary as these models are trained differently and require regulatory oversight to ensure safety and protect patient privacy. This paper provides practical recommendations for regulators to bring this vision to reality.

NPJ DIGITAL MEDICINE (2023)

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Review Computer Science, Artificial Intelligence

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

Rodolfo Stoffel Antunes et al.

Summary: The use of machine learning with electronic health records is gaining popularity for improving healthcare decision-making. Federated learning is a promising solution to protect data privacy and improve trustworthiness. However, there are still many challenges to be addressed before widespread adoption.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2022)

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Review Biochemistry & Molecular Biology

Multimodal biomedical AI

Julian N. Acosta et al.

Summary: This Review discusses the potential applications, technical pitfalls, and challenges of multimodal artificial intelligence models in the field of health and medicine. By utilizing data from large biobanks, electronic health records, medical imaging, and genomics, multimodal AI solutions have the ability to capture the complexity of human health and disease. Key areas such as personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, and virtual health assistants offer opportunities, but data, modeling, and privacy challenges need to be addressed for the full potential of multimodal AI in health to be realized.

NATURE MEDICINE (2022)

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Article Health Care Sciences & Services