Artificial intelligence for breast cancer: Implications for diagnosis and management

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: 3, P. 100120 - 100120

Published: June 17, 2024

Breast cancer's global impact and high mortality rates drive interest in Artificial intelligence (AI) applications. AI's pattern recognition decision-making abilities offer promise detection, diagnosis, personalized treatment, risk assessment, prevention. Screening early detection are improved by AI-enhanced mammography. AI aids radiologists lesion though concerns about false positives persist. In addition, revolutionizes breast imaging, assisting reading mammograms, biomarker lymph node outcome prediction. Genetic insights into treatment response advanced AI, particularly through deep learning algorithms. Collaborative approaches benefit from AI-guided radiotherapy planning. However, challenges of include data privacy, ethics, regulatory issues that must be navigated to ensure successful implementation while upholding healthcare trust. Therefore, this commentary provided an overview implication cancer.

Language: Английский

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Model‐Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing

Clinical Pharmacology & Therapeutics, Journal Year: 2024, Volume and Issue: 116(3), P. 619 - 636

Published: July 11, 2024

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize risks in each patient, is essential for drugs with a narrow window severe adverse effects. Adaptive dosing strategies extend precision concept time-varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics decision-making process where clinicians adapt administration response evolution monitoring. This paper aims investigate potentiality coupling RL population PK/PD models develop algorithms, reviewing most relevant works field. Case studies were integrated within algorithms as simulation engine predict consequences any action have been considered discussed. They mainly concern propofol-induced anesthesia, anticoagulant therapy warfarin variety anticancer differing administered agents and/or monitored biomarkers. The resulted picture highlights certain heterogeneity terms approaches, applied methodologies, degree adherence clinical domain. In addition, tutorial how problem should be formulated key elements composing framework (i.e., system state, agent actions reward function), could enhance approaches proposed readers interested delving Overall, integration into RL-framework holds great promise but further investigations advancements are still needed address current limitations applicability methodology requiring adaptive strategies.

Language: Английский

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Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease

Molecules, Journal Year: 2024, Volume and Issue: 29(21), P. 5131 - 5131

Published: Oct. 30, 2024

The most prevalent chronic neurodegenerative illness in the world is Alzheimer's disease (AD). It results mental symptoms including behavioral abnormalities and cognitive impairment, which have a substantial financial psychological impact on relatives of patients. review discusses various pathophysiological mechanisms contributing to AD, amyloid beta, tau protein, inflammation, other factors, while emphasizing need for effective disease-modifying therapeutics that alter progression rather than merely alleviating symptoms. This mainly covers medications are now being studied clinical trials or recently approved by FDA fall under treatment (DMT) category, alters targeting underlying biological DMTs focus improving patient outcomes slowing decline, enhancing neuroprotection, supporting neurogenesis. Additionally, amyloid-targeting therapies, tau-targeting neuroprotective others. evaluation specifically looked at studies FDA-approved novel Phase II III development were carried out between 2021 2024. A thorough US government database identified biologics small molecule drugs 14 agents I, 34 II, 11 might be completed 2028.

Language: Английский

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Reinforcement Learning and PK‐PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma

Clinical Pharmacology & Therapeutics, Journal Year: 2024, Volume and Issue: 115(4), P. 825 - 838

Published: Feb. 9, 2024

The integration of pharmacokinetic‐pharmacodynamic (PK‐PD) modeling and simulations with artificial intelligence/machine learning algorithms is one the most attractive areas pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, paper presents evaluates a new framework embedding PK‐PD models into reinforcement (RL) algorithm, Q‐learning (QL), personalize pharmacological treatment. Each patient represented set parameters has personal QL agent optimizes individual training phase, leveraging simulations, assesses different actions, defined consistently clinical knowledge consider only plausible dose‐adjustments, in order find optimal rules. proposed was evaluated optimize erdafitinib treatment patients metastatic urothelial carcinoma. This drug approved by US Food Drug Administration (FDA) dose‐adaptive protocol based on monitoring levels serum phosphate, represent biomarker both efficacy toxicity. To evaluate flexibility methodology, heterogeneous virtual population 141 generated using an PK (PopPK)‐PD literature model. For each patient, response simulated QL‐optimized one. agents outperform rules, increasing more than 10% safety at end point. Results confirm great potentialities PopPK‐PD RL dosing tasks.

Language: Английский

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Microbiome-based dietary supplements for better development and healthy brain

International review of neurobiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK‐PD Models: Application to Givinostat Treatment of Polycythemia Vera

CPT Pharmacometrics & Systems Pharmacology, Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Language: Английский

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Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology

Clinical Pharmacology & Therapeutics, Journal Year: 2023, Volume and Issue: 114(3), P. 578 - 590

Published: July 1, 2023

The promise of transforming digital technologies into treatments is what drives the development therapeutics (DTx), generally known as software applications embedded within accessible technologies—such smartphones—to treat, manage, or prevent a pathological condition. Whereas DTx solutions that successfully demonstrate effectiveness and safety could drastically improve life patients in multiple therapeutic areas, there general consensus generating evidence for presents challenges open questions. We believe are three main areas where application clinical pharmacology principles from drug field benefit development: characterization mechanism action, optimization intervention, and, finally, its dosing. reviewed studies to explore how approaching these topics better characterize associated with them. This leads us emphasize role play advocate approach merges such traditional important considerations highly attractive fast‐paced world solutions.

Language: Английский

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Artificial Intelligence in Emotion Quantification : A Prospective Overview

CAAI Artificial Intelligence Research, Journal Year: 2024, Volume and Issue: unknown, P. 9150040 - 9150040

Published: Aug. 22, 2024

Language: Английский

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Quantitative systems pharmacology in the age of artificial intelligence

CPT Pharmacometrics & Systems Pharmacology, Journal Year: 2023, Volume and Issue: 12(12), P. 1823 - 1826

Published: Oct. 1, 2023

In this perspective, we briefly review the state-of-the-art covering interface between quantitative systems pharmacology (QSP) and artificial intelligence (AI) machine learning (ML); in particular, how AI/ML are conceived used as many attempts to address methodological pain points of QSP. a second part, invite reader step out from discipline-centric view discuss paradigm shift consisting repurposing AI into 2022, Journal Pharmacokinetic Pharmacodynamic published special issue edited by Cho, Zhang, Bonate with 10 scientific articles illustrating ways for coupling (AI). The Zang et al. summarizes current state-of-the-art1 explains that AI/machine (ML) currently four main applications related QSP: parameter estimation, model structure, complexity reduction, virtual population generations (see Figure 1). What may strike is – although there good reasons think improving on such technical domains will increase impact QSP these speak toward dimensions discipline. Estimation parameters, writing down optimal structures, reducing complexity, or properly generating terms which talk modeling experts but probably only them. Parameter estimation known be an because models often too large respect available data. modelers typically working engineers; equations describe complex high level granularity, elementary biological processes unknown uncertain. addition that, clinical end aim predict remote expend effort accurately describe. We, community, trying issues AI/ML. On one hand, big data, omics and/or imaging can treated inputs supervised parameters outputs ref. 2 explanation approach oncology). other could linked endpoints similar formalism where predicted. Finding structural also issue, often, structure designed hand; thus subjected subjectivity coming whomever wrote it. We use data-driven approaches inform through Boolean networks, Bayesian even physics-based AI. has received attention especially safety prediction, shown recent initiative identify molecular players involved drug-induced peripheral neuropathy.3 Regarding authors propose apply feature selection techniques reduce priori lead parsimonious more amenable tasks 1 further information). Last not least, creation populations (VPs) been rightly focus much interest ML, prevalence weighting Markov Chain Monte Carlo, proposed relevant plausible ensemble order generate patients resembling cohorts.4 speed up simulations computational-demanding efficient VP generation.5 key drug development safe medicine, two them immediately come mind. First, multiscale nature points, some areas incorporate aspects daily living, dementia rating Alzheimer's disease (AD) unified Parkinsons's scale (UPDRS). Leveraging data notoriously challenging. This difficulty spread therapeutic given increasing availability patients' reported outcomes. sometimes highlight problem being "subjective." author's opinion, less than multidimensional measures. lacking methods bridge traditional level, our models. means have urgency double innovation progression way descriptive information contained predict. Second, lack validated biomarkers, responsibility look beyond classical markers measured blood tissue potential surrogates action. Still, field neuroscience, would cognitive, behavioral, functional levels. How do we, points? news repurposed/reframed stick fundamental focus. Indeed, several recently reports suggest taking advantage algorithm capability perform well human basis "mechanistic" An illustrative example around reframing algorithms performing at facial recognition their analysis shed new light mechanisms happening suffering AD losing capabilities.6 After having trained (mimicking healthy individual) perturbed different ways, either modifying weight mimic brain signal weakening cutting neural network nodes lesions. Simulations performed results were put perspective biomarker neuroimaging initiative. paper presents very clearly. However, deep interpretability, question whether indeed "reverse engineering" informative actual pathological remains fully open. Following strategy technique called reinforcement (RL) promising supported theoretical arguments. it experimentally RL valid analyzing humans animals' learning. It following words Richard Sutton Andrew Barto start book RL7: When infant learns walk, fall try again, no explicit teacher does direct sensorimotor connection its environment. Exercising produces wealth about cause effect, consequences actions, what achieve goals. mimics process consists knowing map situations actions so maximize numerical reward signal. represented Decision Process agent takes sense environment, clarity goal achieve. "computerized" led including health. Computationally, aims estimating value function iteratively, experience, updated term "temporal difference" (TD). TD difference currrent expectation state is, after experience. If positive, increased, if negative, should then decreased. happen until convergence. highlighted interesting precision dosing non-pharmacological interventions computational psychiatry.8 Before reporting definition psychiatry, let us illustrate examples. 2004 Science, David Redish repurposed simulate addiction.9 previous concept TD, addiction modeled always positive when specific taken (i.e., subject addicted to). By way, result corresponding value. turn, most likely chosen subject. Earlier, discussed need biomarkers useful analyze cognitive testing 8 examples). Computational psychiatry nascent area defined characterize mental dysfunction aberrant computations. coupled (pharmacological) description intervention, nothing else approach. To envisioned today reinforces "system" component highly beneficial areas, neurodegeneration limited earlier. conclusion, see topic interfacing first inner view, QSP-centric, leverage seems underdeveloped box reframe (Figure 2). Embracing make critical advance modalities. doing tremendous role supporting therapy. Recently, modality emerging, digital therapeutics (DTx), software treat, manage, prevent condition. DTx considered modalities behaviors, disorders, sleep pain. reviewed identified application principles greatly benefit DTx, better characterizing mechanism action, optimize intervention content, right dose patients.10 drive modalities, becoming instrumental overcoming another limitation field, namely selectivity candidates reflecting interactions circuits. So, challenge? author acknowledges Dr. Cristina Santini UK invitation present 4th Exchange Workshop July 12, 2023; workshop participants valuable questions feedback helped perspective. thanks Lucy Hutchinson exchanges help finalizing manuscript. No funding was work. employee shareholder F. Hoffmann-La Roche Ltd.

Language: Английский

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Reinforcement learning-based adaptive deep brain stimulation computational model for the treatment of tremor in Parkinson’s disease

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126154 - 126154

Published: Dec. 1, 2024

Language: Английский

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