Support vector machines (SVMs) are widely utilized in health care research for tasks such as classification, regression, and outlier detection. These models function by developing hyperplanes that maximize the separation between different classes in a feature space, enabling accurate prediction and classification. SVMs are classified into linear, nonlinear (eg, kernel-based), and multiclass variations. Several orthopedic and plastic surgery studies have found success in using SVMs for diagnosis and outcome prediction. While their robustness makes them effective for high-dimensional datasets, SVMs are not without limitations, and future work will be of benefit to strengthen an already powerful and popular technique.

A blockchain is a distributed database that maintains a continuous list of records, which became mainstream with the introduction of bitcoin. It can transform health care and the way patients and surgeons interact. Blockchain can empower patients to manage their own personal health information and allows a smoother transition between different providers and health care systems. Blockchain can also be used to audit research to ensure authenticity. Furthermore, the universality of cryptocurrency can ease financial transactions. Blockchain's biggest limitation is approval through privacy laws and health care workers' understanding of its benefits and protection of patient privacy.

Natural language processing systems (NLPs) and large language models (LLMs) have the potential to revolutionize hand surgery by streamlining workflows, enhancing patient-care, and advancing research capabilities. NLPs are particularly adept at processing unstructured clinical data, enabling more efficient research, quality improvement initiatives, and patient safety monitoring. LLMs contribute through patient education, real-time clinical decision support, and reducing administrative burdens with automated documentation. However, challenges must be resolved to fully realize their benefits. Overcoming these barriers could pave the way for transformative advancements in hand surgery and health care.

Artificial intelligence is transforming medical research and health care, with transformers playing a crucial role in advancing medical data analysis. Originally developed for natural language processing, transformers have successfully applied their self-attention mechanism to image and biological sequence analysis. However, their use in the medical field faces challenges such as data quality, interpretability, ethical issues, and resource demands.

Since the conception of the artificial neuron in 1943, neural networks have developed into multi-layer models enabling image recognition, speech recognition, personalized recommendation for web browsing, social media content, and virtual assistants. Harnessing this power, researchers have developed models that can potentially improve both access to care and efficiencies in health care delivery. The goal of this study is to provide a fundamental understanding of neural networks to hand surgeons with current examples in various medical subspecialties.

Personalized medicine harnesses individuals' genetic, environmental, and lifestyle information to deliver targeted treatment strategies. Driven by advances in genomics and the integration of artificial intelligence (AI), this approach enables precise diagnosis, optimized therapeutics, and improved patient outcomes. AI-driven tools, such as predictive analytics, machine learning, and real-time monitoring, can facilitate early detection of diseases, enhance drug discovery, and support adaptive therapies. Future enhancements in AI-driven personalized medicine promise to transform the conventional one-size-fits-all health care paradigm and, thus, establish more cost-effective, patient-centered modalities capable of improving clinical outcomes and quality of care on a global scale.

Artificial intelligence-enabled remote patient monitoring is transforming hand surgery by using advanced technologies like machine learning and computer vision to track patient recovery in real-time. Wearable devices and sensors collect objective data, enabling early detection of complications and personalized rehabilitation protocols. Accelerated by the COVID-19 pandemic, these technologies can potentially replace in-person visits, offering tailored-treatment options through data-driven insights.

Artificial intelligence (AI) holds significant promise as a diagnostic and therapeutic adjunct, and it is being rapidly employed in health care. Implementation, however, has been fraught with ethical challenges, with several unique to the field of surgery. Here, the authors provide an introduction to surgical ethics, summarize current policy surrounding health AI, and discuss the ethical and policy challenges of implementing AI in surgery. As the field of AI continues to expand and permeate surgical practice, an understanding of these challenges and core ethical principles is imperative for the safe and effective implementation of these tools.

Personalized treatment enhances healthcare by tailoring optimal decisions to each patient based on their specific characteristics and treatment history. Reinforcement learning (RL) methods are powerful tools for estimating optimal, data-driven, dynamic treatment decision rules. This article presents a tutorial on Tree-based RL and Multi-Objective Tree-based RL for advancing the estimation of optimal dynamic treatment regimes. Data from the Silicone Arthroplasty in Rheumatoid Arthritis study demonstrate their application in optimizing joint arthroplasty decisions. These methods support personalized, data-driven strategies while balancing competing clinical priorities, aiding clinicians in making informed, patient-centered decisions within ethical and practical constraints.

Artificial intelligence, particularly machine learning (ML), has great potential in improving patient outcomes through clinical decision support systems. ML has the capability to revolutionize patient care by improving diagnostics, treatment personalization, and operational efficiency. This article focuses on the evolution of supervised learning models and their applications, including classification and regression techniques. Challenges such as data quality, ethical concerns, model bias, and privacy issues are discussed, alongside the importance of human-AI collaboration.

Arthroscopic treatment of elbow trauma has advantages such as the enhanced view of the elbow, including joint surfaces and soft tissue. Arthroscopy provides an accurate evaluation of chondral lesions and osteochondral loose fragments. A clear advantage is the minimally invasive nature of arthroscopy, decreasing soft tissue injury compared to open techniques. There are several challenges such as patient positioning, soft tissue status, and hemarthrosis. Arthroscopic-assisted treatment of trauma is performed safely earlier in the learning curve, but published literature has shown that the arthroscopic treatment of trauma has an additional learning curve, to obtain improved results even for expert surgeons.

Understanding the varus posteromedial rotatory instability requires a detailed clinical examination and better radiographic characterization of the anteromedial facet fractures with the aid of 3-dimensional computed tomography humeral subtraction views. A better understanding of these injuries allows for the development of more reliable management algorithms to avoid overtreatment with increased risk of complications or undertreatment with the risk of progressive post-traumatic arthritis. An algorithm based on the detailed assessment of the radiographic features of the varus posteromedial rotatory instability to reliably determine the appropriate nonoperative versus operative management of these subtle injuries is discussed.

Continued elbow instability is a challenging condition accompanied by a myriad of complications. A systematic approach to examination, preoperative planning, and surgical intervention is key to addressing and correcting instability. Repair of the bone and soft tissue defects must be prioritized in restoring elbow stability. Characteristics of the initial injury and index operation are useful for determining appropriate surgical management, which may include fixation, grafting, or arthroplasty. Patients with persistent instability despite these measures should be considered for adjuvant stabilization options, including static or dynamic stabilizers.

The radial head plays a crucial role in maintaining normal elbow kinematics, and radial head arthroplasty (RHA) can help restore normal elbow kinematics after radial head fracture. Most modern implants are made of polished metal alloy with stems that may be smooth, press-fit, or cemented. There is no consensus as to the superiority of smooth versus press-fit stems, and both can result in good clinical outcomes. Cemented stems are no longer indicated in routine RHA. Correct implant positioning is critical, and numerous methods of assessment have been described.

Nonoperative management of the rare stable undisplaced olecranon fracture is routine, with plate fixation recommended for a displaced olecranon fracture associated with elbow instability. Internal fixation is often used for stable displaced olecranon fractures, with tension band wire fixation and plate fixation employed. However, this dogma has been challenged for elderly lower demand patients where poor fixation in osteoporotic bone, a delicate soft tissue envelope, symptomatic metalwork, and the risk of further surgery occur. There is increasing data to support nonoperative management in these patients, even in the presence of displacement or comminution.

Distal humerus fractures are challenging injuries due to their soft tissue envelop, and the complex 3 dimensional anatomy including 3 articular surfaces. Modern fixation principles have allowed for improved outcomes even in previously unreconstructible fracture patterns. Despite these advances bone loss, and articular comminution may require alternative or adjunctive strategies such as grafting, fusion, hemi-arthroplasty and total elbow arthroplasty options.

Post-traumatic elbow arthropathy is a challenging pathology for the upper extremity surgeon to manage. While arthroplasty is a powerful tool for the mnagement of degenerative elbow conditions, it remains optimized for the lower demand, elderly patients due to high revision and complication rates in younger patients or those with post-traumatic pathology. In the younger patient, early post-traumatic symptoms can be managed with joint preserving techniques, however, as disease progresses, joint sacrificing procedures may be required. Unfortunately, there is limited strong evidence to guide treatment; however, careful patient centered discussion and treatment selection can produce successful outcomes.

Nerve injury about the elbow is a commonly reported problem after upper extremity trauma and surgery. Understanding the complete workup and management of these injuries is crucial in preserving upper extremity function and ensuring high-grade nerve injuries are diagnosed and treated in a timely manner. Assessment begins with a thorough history and physical examination, along with required imaging to evaluate bony or vascular injury as needed. Patients with neurological deficits in the surgical field of bony fixation or evidence of high-grade nerve injury on imaging may be candidates for early exploration. Otherwise, patients are observed longitudinally for signs of nerve recovery with both physical examination and electrodiagnostic tests.

Comprehensive preoperative assessments and multidisciplinary collaboration are crucial for crafting tailored treatment plans for complex elbow soft tissue injuries. Generally, surgeons should prioritize the simplest effective method from the reconstruction ladder, ensuring durable wound coverage and joint function preservation. Advance postoperative rehabilitation planning is integral, emphasizing early mobilization and a patient-centered approach for optimal healing and functional restoration.