# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 4 Challenges and Limitations  
### QC Challenges  
QC holds immense promise for transforming medical research, but several challenges remain that must be addressed to fully realize its potential [86]. One of the most significant \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Description** & **Reference** \\ \hline Drug Discovery & QC can enhance drug discovery by enabling simulations of complex molecular interactions at the quantum level. & [34] \\ \hline Radiotherapy & QC excels at solving optimization problems, which is crucial for radiotherapy treatment planning. & [35] \\ \hline Genomic Data Analysis & QC has the potential to innovate genomic data analysis by enabling faster processing and more accurate predictions. & [37] \\ \hline Medical Imaging & ML algorithms are increasingly used in medical imaging and pathology to assist in diagnosing diseases with higher accuracy and speed. & [39] \\ \hline Digital Pathology & ML algorithms are applied to analyze histopathological slides of tissue samples, helping pathologists identify disease markers, cancerous cells, and other abnormalities. & [41] \\ \hline Predictive Modeling & ML models can identify risk factors and predict the likelihood of disease progression or recurrence. & [43] \\ \hline Personalized Medicine & ML algorithms can analyze genetic mutations and molecular profiles of tumors to recommend targeted therapies. & [47] \\ \hline Quantum ML (QML) & QML refers to the application of QC to enhance traditional ML algorithms. & [51] \\ \hline Hybrid Quantum-Classical Models & Researchers are exploring hybrid quantum-classical models, where quantum algorithms are integrated with classical ML methods. & [58] \\ \hline LLM-based Chatbots & LLM-based chatbots are increasingly being used in patient education, providing individuals with personalized, accessible information about their conditions, treatments, and medications. & [72] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of applications in medical decision-making. barriers is the current state of quantum hardware. Scalability remains a critical issue; while existing quantum computers are capable of executing specific algorithms, they are far from being powerful enough to handle the vast complexities of real-world medical datasets [87]. In addition, high error rates in quantum computations hinder accuracy and reliability. Quantum systems are inherently susceptible to noise, which can cause decoherence--the loss of quantum information due to interactions with the surrounding environment [88]. Overcoming these limitations requires advancements in qubit stability, error correction protocols, and the development of fault-tolerant quantum architectures. Another major challenge is the accessibility of QC resources for medical researchers [89]. Quantum computers are expensive and require specialized infrastructure, such as ultra-low-temperature environments, to operate. These requirements limit their availability to a few well-funded institutions and organizations. As a result, many medical researchers lack the opportunity to experiment with quantum techniques or develop applications tailored to their specific needs [90]. Furthermore, the steep learning curve associated with quantum programming languages and frameworks adds another layer of difficulty, creating a gap between theoretical possibilities and practical implementation in the medical field [91]. Quantum simulators, such as IBM's Qiskit, play a crucial role in the development and evaluation of QML algorithms [92]. These simulators allow researchers to test QML approaches in a controlled environment before deploying them on actual quantum hardware, which is currently constrained by factors such as limited qubit coherence times, gate errors, and noise. While Qiskit provides access to real quantum processors, most QML studies rely on simulations due to the practical challenges of running large-scale algorithms on existing hardware.