Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

Pinton, Philippe

doi:10.1080/07853890.2023.2300670

Cited by 7 publications

(1 citation statement)

References 73 publications

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“…We recommend taking up the developments by Morikubo et al [14] and Miyoshi et al [15] given their innovative and useful predictive tools, which may help clinicians select the optimal treatment for patients with UC in the near future [20]. We provide perspectives regarding the generalizability and applicability of their models to other populations or settings.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models

Pinton

2024

Diagnostics

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Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare.

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Section: Analysis and Discussionmentioning

confidence: 99%

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models

Pinton

2024

Diagnostics

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Exploring intimacy and family planning in Inflammatory Bowel Diseases: a qualitative study

Gabova,

Bednarikova,

Meier

et al. 2024

Annals of Medicine

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Background Inflammatory bowel diseases (IBD), comprising ulcerative colitis and Crohn’s disease, have a profound impact on people’s lives. This study aims to investigate the perceived impact of IBD on sexual life and family planning to enhance understanding of the interplay between IBD, sexuality, intimacy and family planning to foster a better quality of life for those living with the condition. Materials and methods The study used the qualitative DIPEx method (Database of Personal Experiences of Health and Illness) developed by the Health Experiences Research Group at the University of Oxford, standardized for the Czech Republic. Semi-structured interviews with 36 participants (19 with Crohn’s disease, 17 with ulcerative colitis) were conducted, employing open coding and thematic analysis. The maximum variation sampling was utilized, considering various factors, such as disease stage, medications, treatments, age, age at diagnosis and sociodemographic characteristics. Results Five main topics emerged: sexual activity, body image and discomfort, partner relationships, family planning, and the role of gastroenterologists in family planning decisions. Participants’ perceptions of sexuality varied, with some experiencing reduced sexual activity during acute phases of the disease. IBD had contrasting effects on partner relationships, and family planning was an important concern, with women valuing support and collaboration with their gastroenterologists in making pregnancy decisions. Information dissemination and open communication from professionals are highly needed. Conclusions This is the first study concerning the sexuality and family planning of people with IBD in the Czech Republic. It highlights the need for information and open communication from professionals.

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From prevention to management: exploring AI’s role in metabolic syndrome management: a comprehensive review

Choubey,

Upadrasta,

Kaur

et al. 2024

Egypt J Intern Med

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Background This review aims to comprehensively explore the integration of artificial intelligence (AI) in the prevention, diagnosis, and treatment of metabolic syndrome (MetS). MetS is characterized by a cluster of conditions, posing a growing public health threat globally. Recognizing the limitations of traditional management approaches, we emphasize the potential of AI in transforming the management of MetS, focusing on recent advancements and applications in risk prediction and diagnosis. Body and conclusion. The integration of artificial intelligence in medicine is expanding, particularly in managing MetS, involving conditions like hypertension and dyslipidemia. Diagnosis and treatment challenges stem from addressing multiple conditions simultaneously. AI tools prove essential in monitoring indices such as blood pressure and glucose, and identifying trends for treatment adjustments. Lifestyle modifications are crucial, and AI can facilitate these changes through user-friendly interfaces and positive reinforcement. Standardization and successful implementation of AI tools in medical practices are necessary for revolutionizing MetS management, requiring focused future research efforts.

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Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

Cited by 7 publications

References 73 publications

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models

Exploring intimacy and family planning in Inflammatory Bowel Diseases: a qualitative study

From prevention to management: exploring AI’s role in metabolic syndrome management: a comprehensive review

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