The CSS was accompanied by a natural language explanation of the scores. The LLM judge role used GPT-4.0 by OpenAI. Evaluation by Human Experts Each encounter pair in which the top diagnosis of AI and clinician did not match was evaluated by a board-certified physician with access to medical reference material. Blinding the physician to the origin of the documentation proved impractical, as the AI-based notes were highly consistent and thus easily recognized within a few pairs. The physician was asked to determine the cause of the disagreement between the documents, whether AI or the physician was more likely to be correct, whether it was not possible to determine which diagnosis was more appropriate, and whether the diagnoses did, in fact, match. Similarity and Style Metrics To evaluate how similar-or different the AI-generated (Doctronic) and clinician-generated SOAP notes were, we followed a two-step process. First, we assessed surface-level textual similarity using three standard statistical metrics: (1) TF IDF cosine similarity, which transforms each note into a weighted term-frequency vector and measures the cosine of the angle between them to capture word-frequency alignment; (2) the Jaccard index, which is the ratio of the intersection to the union of lowercased token sets, ranging from 0 (no overlap) to 1 (identical token sets); and (3) the Levenshtein ratio, a normalized edit-distance score based on character-level insertions, deletions, and substitutions that quantifies textual similarity on a 0-1 scale. These analyses demonstrated only minimal alignment in phrasing, formatting, and vocabulary. Then, to probe contextual and semantic similarity, we generated embeddings for each note using OpenAI's text embedding 3 small model and two versions of Biobert,

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.

This is a pre-print version of an article published as: Hatherley, Joshua. Please cite that version. 2 Abstract: Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI's progress in medicine, however, has led to concerns regarding the potential effects of this technology upon relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied upon, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely upon AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. State-of-the-art and state-of-the-practice AI model evaluations are limited to laboratory studies on medical datasets or direct clinical trials with no or solely patient-centered controls. Moreover, the crucial role of clinicians in collaborating with AI, pivotal for determining its impact on clinical practice, is often overlooked. For the first time, we emphasize the critical necessity for rigorous and cost-effective evaluation methodologies for AI models in clinical practice, featuring patient/clinician-centered (dual-centered) AI randomized controlled trials (DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an effective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-phase inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results demonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI. Notably, VC-MedAI performs comparably to human clinicians, replicating insights and conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner. Chinese Clinical Trial Registration: ChiCTR2400086816.

Artificial intelligence (AI) is now able to detect cognitive distortions from text messages. A new study published in Psychiatric Services, an American Psychiatric Association peer-reviewed journal, shows how AI natural language processing (NLP) can detect cognitive disorders in texts as effectively as human clinicians. "Recent advancements in mobile phone–based mental health interventions, combined with advancements in computational methods of language analysis, have created new possibilities for developing technology-assisted interventions," wrote the researchers from the University of Washington School of Medicine. The American Psychological Association defines cognitive distortion as thinking, beliefs, or perceptions that are either false or flawed that can happen to all people to some degree. There are numerous ways a person's thoughts may be inaccurate or skewed.

Harvard Medical School scientists and colleagues at Stanford University have developed an artificial intelligence diagnostic tool that can detect diseases on chest X-rays directly from natural-language descriptions contained in accompanying clinical reports. The step is deemed a major advance in clinical AI design because most current AI models require laborious human annotation of vast reams of data before the labeled data are fed into the model to train it. A report on the work, published Sept. 15 in Nature Biomedical Engineering, shows that the model, called CheXzero, performed on par with human radiologists in its ability to detect pathologies on chest X-rays. The team has made the code for the model publicly available for other researchers. Most AI models require labeled datasets during their "training" so they can learn to correctly identify pathologies. This process is especially burdensome for medical image-interpretation tasks since it involves large-scale annotation by human clinicians, which is often expensive and time-consuming.

Jana Denzel explores artificial intelligence and the role it will play in the world of dentistry. Movies such as The Terminator and I, Robot hauntingly illustrate futuristic worlds shaped by technological innovations that arose from artificial intelligence (AI). However, that scientific-fiction AI is what's known as'artificial general intelligence'. In other words, computer intelligence almost identical from human intelligence – and, to date, it remains very much a fantasy. 'Narrow AI' is the label that scientists give to the AI that's incorporated into our world today.

In recent years, there has been a proliferation of patient outcome prediction research that applies machine learning (ML) and artificial intelligence (AI) to …

Wikipedia defines artificial intelligence in healthcare as the use of complex algorithms and software to emulate human cognition in the analysis, interpretation and comprehension of complicated medical and healthcare data. This "emulation" is done in less time and at a fraction of the cost. Artificial intelligence in healthcare was valued at about $600 million in 2014 and is projected to reach $150 billion by 2026. Reinventing and reinvigorating healthcare through the use of artificial intelligence is happening predominantly through assisting in better diagnosis, better processes, drug development and robot-assisted surgery. In 2015 misdiagnosing illness and medical error accounted for 10% of all U.S. deaths.

AI and related advancements are progressively playing the role of a disruptor in business and society. The application of AI is also increasing in the healthcare domain. These advances can possibly change numerous parts of patient care, just as regulatory procedures inside supplier, patient experience, and pathology labs. There are as of now various researches recommending that AI can proceed just as or better than people at key human services, for example, diagnosing the ailment. Today, algorithms are beating radiologists at spotting harmful tumors.