Predicting and explaining with machine learning models: Social science as a touchstone.

Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful - at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap.

A paradigm shift?-On the ethics of medical large language models.

After a wave of breakthroughs in image-based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent-due to most recent staggering successes of large language models-from single-purpose applications toward generalist models, driven by natural language. This article investigates the implications of this paradigm shift for the ethical debate.

Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice.

In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss different sources of uncertainty arising for clinicians trying to evaluate the trustworthiness of algorithmic evidence when making diagnostic judgments.

Enabling Fairness in Healthcare Through Machine Learning.

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups.

Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.

This paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection.

On Algorithmic Fairness in Medical Practice.

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare.

How competitors become collaborators-Bridging the gap(s) between machine learning algorithms and clinicians.

For some years, we have been witnessing a steady stream of high-profile studies about machine learning (ML) algorithms achieving high diagnostic accuracy in the analysis of medical images. That said, facilitating successful collaboration between ML algorithms and clinicians proves to be a recalcitrant problem that may exacerbate ethical problems in clinical medicine. In this paper, we consider different epistemic and normative factors that may lead to algorithmic overreliance within clinical decision-making.

Randomised controlled trials in medical AI: ethical considerations.

In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials-particularly randomised controlled trials (RCTs)-is gaining ground.

On the ethics of algorithmic decision-making in healthcare.

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level.

Secondary immune thrombocytopenia (ITP) as an initial presentation of Whipple's disease.

Immune thrombocytopenia (ITP) is a heterogeneous autoimmune disease characterized by low platelet count that has been associated with a number of chronic infections but rarely described as a manifestation of Whipple's disease (WD). We present a case of Whipple's disease in a patient initially diagnosed with ITP. A 46-year old male in the fifth decade of life presented with presumed idiopathic ITP and was treated with several therapies including corticosteroids, rituximab, and thrombopoietin receptor agonists.

Targeting Lymph Node Retrieval and Assessment in Stage II Colon Cancer: A Quality Outcome Community-Based Cancer Center Study.

Purpose: Adequate lymph node evaluation is required for the proper staging of colon cancer. The current recommended number of lymph nodes that should be retrieved and assessed is 12.

Methods: The multidisciplinary Gastrointestinal Tumor Board at the Derrick L.

Infusion of palonosetron plus dexamethasone for the prevention of chemotherapy-induced nausea and vomiting.

Serotonin (5-HT3) receptor antagonists are the foundation of standard antiemetic care for cancer patients receiving emetogenic chemotherapy. To enhance the efficacy of these supportive care agents, dexamethasone is routinely admixed with the 5-HT3 receptor antagonist, which is administered by intravenous infusion before chemotherapy begins. This phase II study evaluated the safety and efficacy of intravenous palonosetron admixed with dexamethasone to prevent chemotherapy-induced nausea and vomiting (CINV) in patients receiving moderately emetogenic chemotherapy.

Combination therapy for chemotherapy-induced nausea and vomiting in patients receiving moderately emetogenic chemotherapy: palonosetron, dexamethasone, and aprepitant.

The objective of this multicenter, phase II, open-label study was to evaluate the safety and efficacy of the newest 5-hydroxytryptamine3 (5-HT3) receptor antagonist, palonosetron, plus dexamethasone and aprepitant in preventing nausea and vomiting in patients receiving moderately emetogenic chemotherapy. Eligible patients received a single intravenous dose of palonosetron (0.25 mg on day 1 of chemotherapy), along with 3 daily oral doses of aprepitant (125 mg on day 1,80 mg on days 2 and 3) and dexamethasone (12 mg on day 1,8 mg on days 2 and 3).

Postoperative upper airway obstruction after recovery of the train of four ratio of the adductor pollicis muscle from neuromuscular blockade.

Anesthetics, and even minimal residual neuromuscular blockade, may lead to upper airway obstruction (UAO). In this study we assessed by spirometry in patients with a train-of-four (TOF) ratio >0.9 the incidence of UAO (i.

Efficacy and safety analysis of epoetin alfa in patients with small-cell lung cancer: a randomized, double-blind, placebo-controlled trial.

Purpose: This randomized, double-blind, placebo-controlled trial (N93-004) evaluated the effects of epoetin alfa on tumor response to chemotherapy and survival in patients with small-cell lung cancer (SCLC).

Patients And Methods: Adult patients with hemoglobin < or = 14.5 g/dL starting chemotherapy received epoetin alfa 150 U/kg or placebo subcutaneously 3 times weekly until 3 weeks after completion of chemotherapy.