Creative and generative artificial intelligence for personalized medicine and healthcare: Hype, reality, or hyperreality?

Shaban-Nejad, Arash; Michalowski, Martin; Bianco, Simone

doi:10.1177/15353702241226801

Cited by 2 publications

(1 citation statement)

References 24 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of DL models to create texts, images, and other content by leveraging information from training datasets is referred to as generative AI. The applications of this type of AI in the healthcare sector are developing fast, and in the near future, such models (e.g., large language models-LLMs) may play a role in analyzing different types of data, including medical imaging and RWD, protein structure prediction, clinical documentation, diagnostic assistance, clinical decision support, and drug design (Shaban-Nejad et al, 2023). However, to date, generative AI has unpredictable fluctuations in terms of quality and accuracy, as well as some ethical issues that could limit its full application in the healthcare sector, thus highlighting the need for human supervision (Zhang and Kamel Boulos, 2023).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

Crisafulli,

Ciccimarra,

Bellitto

et al. 2024

Front. Drug Saf. Regul.

View full text Add to dashboard Cite

In recent years, there has been an exponential increase in the generation and accessibility of electronic healthcare data, often referred to as “real-world data”. The landscape of data sources has significantly expanded to encompass traditional databases and newer sources such as the social media, wearables, and mobile devices. Advances in information technology, along with the growth in computational power and the evolution of analytical methods relying on bioinformatic tools and/or artificial intelligence techniques, have enhanced the potential for utilizing this data to generate real-world evidence and improve clinical practice. Indeed, these innovative analytical approaches enable the screening and analysis of large amounts of data to rapidly generate evidence. As such numerous practical uses of artificial intelligence in medicine have been successfully investigated for image processing, disease diagnosis and prediction, as well as the management of pharmacological treatments, thus highlighting the need to educate health professionals on these emerging approaches. This narrative review provides an overview of the foremost opportunities and challenges presented by artificial intelligence in pharmacology, and specifically concerning the drug post-marketing safety evaluation.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

Crisafulli,

Ciccimarra,

Bellitto

et al. 2024

Front. Drug Saf. Regul.

View full text Add to dashboard Cite

show abstract

Artificial intelligence in andrology – fact or fiction: essential takeaway for busy clinicians

Calogero,

Crafa,

Cannarella

et al. 2024

Asian Journal of Andrology

View full text Add to dashboard Cite

Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.

show abstract

Creative and generative artificial intelligence for personalized medicine and healthcare: Hype, reality, or hyperreality?

Cited by 2 publications

References 24 publications

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

Artificial intelligence in andrology – fact or fiction: essential takeaway for busy clinicians

Contact Info

Product

Resources

About