Revolutionizing Breast Cancer Care: AI-Enhanced Diagnosis and Patient History.

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study's results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.

Exploring patient experiences and acceptability of group vs. individual acupuncture for Cancer-related pain: a qualitative study.

BACKGROUND: Individual acupuncture (AP) is a safe and effective treatment for cancer-related pain and other symptoms in cancer survivors. However, access to individual AP is limited, and costs can be prohibitive. Group AP could be a more cost-effective alternative as it is less expensive and non-inferior to individual AP for pain relief. Despite growing evidence in favour of group AP, patient acceptability and experience of group AP in cancer patients is relatively unknown. This exploratory study sought to compare patient experiences and acceptability of group versus individual AP in cancer patients. METHODS: Semi-structured, open-ended, in-depth interviews were conducted in a subset of 11 cancer patients enrolled in a randomized non-inferiority trial of group vs. individual AP for cancer pain. Participants for this study were recruited via purposive sampling, aiming for diversity in age, sex, education, employment, cancer types, and treatment arms. Data was analyzed using inductive thematic analysis. RESULTS: Two major themes were identified: a) overall experience of AP treatment b) value of AP. Participants across both treatment arms acknowledged improvement in pain, quality of sleep, mood and fatigue. Participants in the group AP arm reported a significant increase in perceived social support, while participants in the individual arm valued privacy and one-on-one interaction with the acupuncturist. Although some participants in the group arm had privacy-related concerns before the commencement of the program, these concerns waned after a few AP sessions. Participants across both the treatment arms reported cordial clinician-patient relationship with the acupuncturist. Willingness to pursue AP treatment in the future was comparable across both the treatment arms and was limited by out-of-pocket costs. CONCLUSION: Patient acceptability and experience of treatment in group AP was on par with individual AP. Group AP may further augment perceived social support among patients and privacy concerns, if any, subside after a few sessions. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT03641222 ). Registered 10 July 2018 - Retrospectively registered.

The machine giveth and the machine taketh away: a parrot attack on clinical text deidentified with hiding in plain sight.

OBJECTIVE: Clinical corpora can be deidentified using a combination of machine-learned automated taggers and hiding in plain sight (HIPS) resynthesis. The latter replaces detected personally identifiable information (PII) with random surrogates, allowing leaked PII to blend in or "hide in plain sight." We evaluated the extent to which a malicious attacker could expose leaked PII in such a corpus. MATERIALS AND METHODS: We modeled a scenario where an institution (the defender) externally shared an 800-note corpus of actual outpatient clinical encounter notes from a large, integrated health care delivery system in Washington State. These notes were deidentified by a machine-learned PII tagger and HIPS resynthesis. A malicious attacker obtained and performed a parrot attack intending to expose leaked PII in this corpus. Specifically, the attacker mimicked the defender's process by manually annotating all PII-like content in half of the released corpus, training a PII tagger on these data, and using the trained model to tag the remaining encounter notes. The attacker hypothesized that untagged identifiers would be leaked PII, discoverable by manual review. We evaluated the attacker's success using measures of leak-detection rate and accuracy. RESULTS: The attacker correctly hypothesized that 211 (68%) of 310 actual PII leaks in the corpus were leaks, and wrongly hypothesized that 191 resynthesized PII instances were also leaks. One-third of actual leaks remained undetected. DISCUSSION AND CONCLUSION: A malicious parrot attack to reveal leaked PII in clinical text deidentified by machine-learned HIPS resynthesis can attenuate but not eliminate the protective effect of HIPS deidentification.