Machine learning algorithms in personalized treatment planning

Machine learning algorithms are revolutionizing personalized treatment planning by analyzing patient data to create tailored interventions. These algorithms optimize treatment strategies, improve outcomes, and minimize adverse effects, enabling precision medicine that adapts to individual needs and promotes more effective healthcare delivery.

Keywords:  Machine Learning, Personalized Treatment, Precision Medicine, Treatment Planning, Healthcare Optimization, Patient Data

 3.1. Introduction

In medical practice, “one size does not fit all patients.” Healthcare has been evolving towards personalized methods in which treatment procedures are adapted to fit the individual characteristics and needs of the patient. The widespread use of information and communication technologies has allowed for the accumulation of data from various sources such as Electronic Health Records, intake/exclusion criteria for clinical trials, computerized physician order entry, diagnostic reports, and medical claims. This allows us to leverage modern machine-learning technologies for the assessment and refinement of treatment plans for patients. Machine learning algorithms combined with expanded knowledge of a patient's data history could provide a critical empirical basis for the identification of previously unknown patient sub-classifications that could evolve our knowledge from evidence-based methods to personalized methods, from data-driven decisions to learning-driven decisions (Ramanakar et al., 2024).

The integration of machine learning solutions in several medical domains is having an increasingly important impact on the decision-making process, treatment planning, and patient management. The introduction of information and communication technologies has greatly increased the amount, complexity, and sources of data that can be collected. In addition to on-site storage, cloud-based computing has begun to provide a high level of computational capability on demand and has sparked new crowd-shared and community-based computing initiatives and services. The goal of this essay is to explore the intersection between machine learning algorithms and personalized treatment planning. We will give an overview of the personalized treatment planning algorithm types such as Random Forest, Lasso Regression, Support Vector Machine, Gradient Boosted Model, Average Individualized Treatment Effects, and Reinforcement Learning.

3.1.1. Background of Personalized Treatment Planning

In efforts to play a major role in patient care and achieve personalized treatment strategies, personalized treatment planning has made significant advancements in recent years. The initial approach to treatment planning consisted of using bulk patient populations with average-based care. LM values are either derived from RCTs where patients are rigidly selected or from population-based cancer registries and observational studies. These values do not translate into optimal treatment strategies for the individual patient as they do not define the individual factors that drive the optimal values.

The development of imaging and computation technology allows the use of functional and anatomical images to infer internal functional motion and/or anatomic geometric change to optimize XRT doses to tumor and normal structures, creating the opportunity to individualize treatment based on anatomic and tumor heterogeneity. Each of these factors may be related to tumor heterogeneity in terms of radiosensitivity or recurrence, given different genetic tumor profiles within the same anatomic entity. Personalization aims at a tailored approach to minimize treatment side effects while still ensuring sufficient therapy efficacy. However, implementing this in a relatively centralized healthcare system adapted to deliver population-based therapy to bulk patient populations is a challenge. This was well illustrated in what has recently been labeled the "regression-to-the-mean effect."

Fig 3 . 1 : Machine Learning for Personalized Treatment.

Traditionally, clinicians have been responsible for patient care and treatment decision-making. Over time, patients have become more advanced in their healthcare knowledge and want a better understanding of their treatment options, risks, and benefits. From a national and international level, the research community appreciates that it is cost-effective when treatment can be individualized to the specific patient populations that will benefit from it based on underlying tumor biology (Syed, 2024). Allowing for treatments to be personalized may help avoid both over- and undertreating patients based on population-level data, as well as highlight patients who may need adjunctive therapy to achieve a cure. Randomized clinical trials have been initiated to become national leaders in computational science, and overcoming these obstacles will need innovative approaches to signal traditional clinical research.

In the best case, patients would be treated based on their genetic profile, lifestyle, environment, and disease stage at the time of consultation. This genetic profile can only be observed over a lifetime; this is when the promise of personalized medicine would come to be: observation of a genetic profile new and unique to the patient that predicts disease risk, cure, etc. Thus, the issues of most categories of personalized medicine have closed their gap with our next-generation healthcare system and become applicable to today's practice. Today's practice is what is referred to in the personalized treatment planning and patient care sections, and it is in this context that the effort was started.

3.1.2. Significance of Machine Learning in Healthcare

Machine learning, or the use of algorithms to find patterns in vast databases, is being increasingly used to drive innovation in sectors like transportation, banking, and media. When it comes to healthcare, machine learning is a valuable tool that processes diverse sources of data like patient records, studies, and medical literature to uncover patterns and problems that may not be readily apparent. These insights can then be used to improve patient outcomes. In some cases, the algorithm itself can even outperform human experts in predicting disease outcomes or drug responses. This not only significantly reduces diagnostic errors, optimizes clinical confidence, and serves patients the most accurate and effective treatment options, but also accelerates novel drug development and clinical trial optimization.

For efficient translation of personalized medicine into the clinic, two factors are crucial—scalability and access. Currently, physicians measure a limited combination of biomarkers to personalize the treatment of cancer. Automating personalized treatment planning by generating comprehensive statistical models from large datasets will improve the scope and coverage of personalized treatment algorithms. Overcoming the economic and time-related constraints of physician involvement in treatment planning decisions using automated machine learning-based tools has the potential to vastly scale personalized strategies. Due to rapidly increasing volumes of digital data, the democratization of machine learning will likely promote the widespread clinical adoption of data-driven clinical decision support. It is ideal for machine learning models to learn from a human-annotated dataset of patient prognosis, where the gold standard of patient anatomy, pathology, and genes is known. However, it will also create ethical challenges in associating diagnosis prediction using solely treatment-agnostic images and patient demographic data. A balance will be needed to de-risk patients and train treatment-centric diagnostic models only on large, ideally public, patient case studies where treatment and outcome anchoring can be incorporated.

3.2. Machine Learning Algorithms in Personalized Treatment Planning

Machine learning is divided into three categories. Supervised learning is an ML algorithm where learning is done by enforcing a mapping between input and output data. In the context of personalized treatment planning, the input is usually the description of a set of patients, and the output is the efficacy of a set of treatment strategies under those circumstances. Unsupervised learning is an ML algorithm where the learning is done by attempting to find structure in a design matrix where each row describes different situations, but where no reference outcome is provided. Reinforcement learning is basically about learning the best action to take under a given set of circumstances to enable a goal to be achieved in the long term under uncertainty concerning the environment (Nampalli et al., 2024).

Some examples of algorithms used in the clinical setting include: classification (supervised learning) - logistic regression, ensemble methods, neural networks, k-nearest neighbors, and support vector machines. Regression (supervised learning) - linear regression, neural networks, decision trees, support vector machines. Clustering (unsupervised learning): k-means. Principal component analysis: performs dimensionality reduction when the training set becomes too large. Deep Q learning (reinforcement learning). In the case of clinical research, the former and latter appear to be the most relevant to the types of problems we must address. The tools differ in many ways, the most visually striking of these being that in supervised learning we have an output variable to be predicted based on input data, while in unsupervised learning we do not. Regulatory approval of these models, together with their accompanying threat of algorithm bias, patient privacy, and quality of the data, are typically the main obstacles.

 Equation 1 : Personalized Prediction Model:

3.2.1. Supervised Learning

A prominent machine learning approach used for personalized treatment planning is supervised learning. This branch of machine learning involves training an algorithm on a labeled dataset to predict or classify new data points. Supervised learning in healthcare can be applied in a range of scenarios; for example, it can be used to predict disease prognosis, anticipate patient outcomes, and predict patient responses to specific treatments, as in the case of treatment planning and prescription in personalized oncology. Supervised learning would also be essential in the prediction of side effects of a certain agent or the type of patients who will respond better to it. Machine learning applications using structured data in personalized medicine have been numerous and successful. This is a very complex task considering that for each patient around 200 different drugs are analyzed.

Different types of algorithms can be individually trained and developed through supervised learning, namely regression models to serve in prediction tasks or classification algorithms to serve in the case of classifying tasks. Unlike unsupervised learning, supervised learning is preferred when more reliable and accurate predictions are required. Despite this, supervised learning has some unique challenges. Perhaps the main limitation is the strong need for a large adequate labeled dataset. Generating those datasets in healthcare can be costly and time-consuming. Furthermore, years of shown patient data to use for model training are seldom available because temporal error perfusion does not usually happen within physiological variation among tissues. Some researchers have attempted to compensate for this drawback of supervised learning applications by developing data-augmentation methods through algorithms to create virtual patient populations in which both observed and non-observed states may be predicted. Certain scenarios—especially those involving PD endpoints—demand repeated observations over time; another reason for the great development of the pharmacometrics from the repeated observed data to conduct the population model analysis.

Fig 3 . 2 : Supervised machine learning in Personalized Treatment.

3.2.2. Unsupervised Learning

Unsupervised learning techniques are used to analyze unlabeled healthcare data. Unlike supervised learning, unsupervised learning focuses on identifying patterns and structures in the data without a predefined output. This can be useful in personalized treatment planning when we want to find clusters of patients that are similar in terms of treatment response or risk factors or to identify data with unusual patterns possibly caused by technical errors or abnormalities. The most well-known method for unsupervised learning is clustering, which is used to group patients together based on their treatment side effects or treatment response. This could potentially lead to new patient groups with shared characteristics and can help to understand which personalized treatment options are available. Other examples of applications of unsupervised learning are dimensionality reduction, patient segmentation, and anomaly detection (Danda, et al., 2024).

Unsupervised learning has several benefits. In particular, the analysis of unlabeled data can lead to new treatment strategies, which is important for personalized treatment planning since most data available in the clinic is unlabeled. Moreover, unsupervised learning algorithms are flexible in their adaptation to new data. They do not need to be retrained each time new molecular or clinical data is available, and they can more easily handle non-curated data where predictions are difficult. Finally, unsupervised learning algorithms provide a more interpretable output as they give more insight into the personalized (sub)groups of patients that are discovered in the data. However, there are also some challenges to unsupervised learning. Interpretation of the results and validation of the patterns that are discovered are of utmost importance since unsupervised learning algorithms can also find patterns that are only present in the training data and do not generalize to the larger patient population.

3.2.3. Reinforcement Learning

Reinforcement learning (RL) is a novel approach in the realm of personalized treatment planning. It is concerned with how agents learn to behave optimally by taking necessary actions. RL is focused on learning by taking action in dynamic environments to achieve the best outcomes over the long run through trial and error. RL has been suggested as a way to simultaneously infer the best treatment and adapt the treatment strategy in real-time using real-time patient data. This novel approach possibly opens a multitude of applications and techniques for investigators. For example, it could involve maximizing the patient’s health under economic constraints, determining the optimal schedule for administering a drug at a given dose, temporalizing lifestyle interventions, and personalized scheduling for physical therapy based on patient progress or recovery goals. However, designing personalized treatment with RL is challenging due to high complexity involving the underlying control system, treatment response models, and data requirements of healthcare systems.

To apply RL for selecting a treatment strategy, the expression of short- and long-term rewards or costs needs to be defined. However, one of the most challenging factors for machine learning algorithms in healthcare is accessing the required data, especially longitudinal data with varying types and frequencies. Moreover, healthcare often consists of multiple interacting patients and healthcare professionals in a real-world environment with rich real-time feedback, making it more complex. Investigators have explored interactive ML strategies for health applications. Adaptive treatment strategies (ATS) offer personalized suggestions to patients at each decision point based on the observed treatment effectiveness and allow interactivity with the patient in recommending the best dosage based on short-term response. RL algorithms are adaptive learning algorithms that can learn from patient feedback and tailor or adapt treatment schedules or intensities over time in a personalized way. RL algorithms may prove to have a higher potential for long-term patient engagement as they interactively adapt over time based on short-term response data as well. Adaptive ML algorithms enable the creation of patient-centered treatments and are based on unique roles. RL algorithms involve learning the optimal policy for the joint state and action space. They may involve providing an optimal sequence of actions based on a sequence of observations. RL also has the property of continuous learning, improving the optimal policy over time. Therefore, in the context of treatment planning, the optimal treatment strategy for the patient is continuously extended from time to time, incorporating new developments in the patient’s condition.

3.3. Applications of Machine Learning in Personalized Treatment Planning

Personalized treatment planning refers to optimizing a treatment strategy for an individual based on their responses and characteristics. A powerful avenue for personalized treatment planning is to use data-driven methods to guide clinical decisions. Machine learning algorithms can be used to build predictive models for individual outcomes for each potential treatment. Consequently, decisions about patient treatment can be oriented toward the best options for that particular patient. These methods stand in contrast to traditional analyses, which average over populations of patients and, therefore, are not directly suitable to inform personalized treatment plans (Syed, 2024).

Areas of Use There are a myriad of therapies for diverse diseases and disorders, but the major therapeutic areas include oncology, cardiovascular disease, mental health, metabolic diseases, infectious diseases, and chronic diseases. There remain many diseases and disorders that need to be addressed. Such methods can be applied to virtually any treatment strategy that can be articulated as a series of decision rules or conditions. The development of precision treatment algorithms has already begun in those areas. For example, several studies have been reported in oncology. The incorporation of clinical trial design into routine clinical decision-making remains one of the primary goals of personalized treatment strategies. These methods can also be applied in extraordinary populations receiving new treatments where clinical trials have not led to regulatory approval. Furthermore, the techniques are particularly useful for diseases where the heterogeneity of the pathological subtypes is not matched by existing treatments. For example, patient responses vary widely for many mental illnesses and chronic diseases. Mental health treatment algorithms have been estimated to be correct only approximately 30% of the time. By better-selecting treatments using patient-specific predictive models, a dual objective – to improve outcomes and also decrease the number of unnecessary treatments – can be met. The selection of an optimal treatment plan is critical to achieve these goals. The use of patient-specific parameters and such models sees a departure from guidelines and an increase in the individualization of treatment. Therefore, machine learning algorithms have a place in the clinical treatment of personalized therapy in the future.

3.3.1. Cancer Treatment Planning

Significant progress has been made in recent years in the ability to collect data at the cellular and molecular levels to develop a mechanistic understanding of the processes underlying diseases such as cancer. Technological advances have been applied in the healthcare domain, and machine learning techniques have allowed us to use this data to develop several recommendations, especially in the area of personalized treatment planning. This is the selection of a treatment carried out with a diagnosis that can be as accurate as possible and aimed at an individual patient. Machine learning enables us to construct predictive models that identify subjects who might be at risk of developing diseases, and who are best treated by known therapies or providing prognostic predictions. To do so, historical data is used to predict the desired outcome. In the town area, using prediction models allows us to find a new patient who is best or most similar to a previous patient and then use the treatment plan applied.

Fig 3 . 3 : Artificial intelligence and Machine Learning in cancer treatment.

Closest to personalized treatment, machine learning has predictive models that can be used to guide the doctor in determining which subjects might benefit from them based on historical records. This work aims to present a series of practical applications of machine learning predictive models in probabilistic personalized cancer treatment planning by describing successful experiences from the case study. We progress in demonstrating, whenever possible, the effectiveness of the implemented methods in clinical settings using Receiver Operating Characteristic areas, positive predictive values, and logarithmic losses. We also describe further applications by taking into account omic/genomic data as covariates of the subjects to personalize their treatment. As an additional objective of the work, we discuss the current challenges of clinical practice from the perspective of this study, emphasizing how machine learning and clinical prediction can contribute to overcoming them. The integration of biological and molecular information, inference of causal relationships, statistical modeling, and personalized prediction aimed at improving patient survival chances are at the current frontier of the subject. However, for all the benefits that biomedical technology can provide, some would argue that making every drug available for every condition for every patient is entirely impractical. Overall, we underline the potential role of this approach in achieving significant progress in medical and clinical success shortly. Quadratic cost is the learning error to be probabilistically minimized: indeed, this cost includes both misclassification regressor errors that will want to minimize in classification and probabilistic terms for those models outputting probabilities directly (Tulasi et al., 2022).

 3.3.2. Mental Health Diagnosis and Treatment

One of the most promising fields of personalized analytics for personalized treatment planning is mental health. Mental health conditions, such as schizophrenia, BPD, and MDD, are disconcerting and traumatic as they are characterized by an ongoing disruption of daily functioning and neural processes. Several well-coordinated large-scale initiatives aim to collect large datasets of patient histories, their biological and genetic characteristics, and their responses to available treatments to identify underlying patterns. A significant characteristic of mental health is that, while most patients with mental health disorders do respond to standard treatments, those treatments do not work optimally for every patient. Insights based on predominantly unsupervised machine learning tools could be used to help identify clinically identifiable domains for precision therapeutics, which is one promising application of the technology in mental health. Furthermore, early diagnosis, couple or family therapy treatment approaches, and administrative programming could all benefit from advanced machine learning techniques in this domain, whether based on qualitative factors emerging from patient demonstration and/or self-reported data.

Predictive models were built to identify MDD patients who responded or did not respond to the standard treatment approaches based on measures from relevant initiatives. The Random Forest and regularized logistic regression models leveraged genetic, fMRI, and other quantitative measures to differentiate remitters and non-remitters from weeks 4-6 treatment visits significantly longer after the start of treatment, likely based on more robust changes. The predictions that these models make offer potential insights about treatment suitability for a given individual based on their characteristics, thereby realizing the potential for personalized care plans. While applicable across different study designs, these applications are dependent on the best design to be utilized and the most critical outcome measure for the population of interest. Limiting the applications of predictive machine learning models to the IDA paradigm also aids with the management of long-term and continuous care, such as managing comorbidities in a way that metabolically healthy individuals on robust treatments are also at a higher risk of developing metabolic comorbidities, as demonstrated by recent studies. The stigma associated with mental health, lack of interest in citizens to label clear stages of a mental health crisis, data privacy, and payment proportions are some of these. In light of ethical considerations and implementation realities, the application of such models in mental health is still in its infancy and deserves the support and endorsement of trial technologists informed about their specific strengths and weaknesses. Our ability to preemptively diagnose and deliver tailored interventions that benefit from very early recovery is in preliminary steps. Concerning the first-person perspective, psychiatry has disabled many mental health sufferers and worsened trust levels; the embedding of this technology into special scenarios would benefit greatly those who need it.

3.4. Challenges and Limitations

Despite the potential, the implementation of machine learning in clinical settings has to overcome several challenges. A main limitation is the security and privacy of healthcare data. Especially when large training and test datasets with real patient data have to be analyzed, the risk of revealing identity or confidential patient information is high. Several studies have already demonstrated that reidentification from different databases is possible. Furthermore, if machine learning algorithms or models learn patient features from electronic health records, the direct link to individual patients has to be handled with caution to protect sensitive data. Different countries have different regulations when handling patient data. For instance, in Europe, the General Data Protection Regulation defines the rules for data protection and privacy. More general politicians and critics in data science, with a focus on ethics, may limit access to data also used for training and assessing the described or other machine learning algorithms.

Another limitation in implementing machine learning in clinical workflows is the 'black box' phenomenon. The more complex a machine learning algorithm is, the harder it is to grasp its underlying structure or process. Even if the analysis reveals a connection between different variables, the relation is often hard to understand, which makes interpreting and explaining that knowledge a challenging task. Consequently, trust and willingness to use machine learning software by clinicians may be reduced. An ethical challenge in implementing machine learning and AI in healthcare is the potential to include and propagate biases. For instance, some studies have already reported that histology data, mammography, and/or genetic information of different races, sexes, and genders have different machine-learning outputs. It has been shown that sex and gender biases, as well as racial/ethnic biases, can also occur in electronic health records, leading to biased automated algorithms for disease diagnosis and severity classification. Discussing these limitations and openly talking about ethical concerns and issues with all stakeholders is necessary to develop machine learning models and software where AI and big data in medicine work conveniently with, and not against, the community (Venkata et al., 2022). One future task will be to find and build a binding consensus in regulations, workflows, and ethical guidelines between data protection laws, the data science community, politicians, doctors, computational scientists, and, of course, patients and citizens in data and AI in healthcare that consider all these issues.

3.4.1. Data Privacy and Security Concerns

The processing of patient data and related sensitive information, such as genetic or clinical data, is not only a source of legal and ethical concerns but is also potentially deeply personal information. Even anonymized data can sometimes be reverse-engineered to expose the original user, who may be harmed by data breaches of any kind. Failure to comply with legal and ethical regulations can result in decreased trust and cooperation and invalidate the results of personalized treatment planning. The regulation and protection of sensitive information are supported by recent laws enacted in member states of the European Union and the United States. There are also several laws and directives governing the use of health data. In the US, the Health Insurance Portability and Accountability Act is generally applicable to machine learning algorithms for personalized treatment planning. Therefore, it is often up to healthcare professionals to handle data ethically.

The applications of machine learning in personalized treatment planning often require large amounts of personal and sensitive information. Therefore, the use of machine learning in a clinical setting could expose laboratories and hospitals to computer attacks. It is of the utmost importance to adopt efficient methods and tools to protect data. Machine learning models can often be dependent on very sensitive features. To comply with several legal and ethical regulations, one often has to be able to prove that the model does not use those features. Removing these features may also decrease the resiliency and robustness of the machine-learning models. Informed consent is a cornerstone of contemporary medical ethics. For personal data to be 'informed,' individuals must be aware of how their data are used and how it is used. Multiple mechanisms can enforce the security of sensitive information, including encryption, generation of synthetic data, splitting the data between servers, and encrypting the client data and transmitting it as ciphertext. Although not perfect, these mechanisms should always be made robust against adversaries attempting to abuse the system. The privacy of these mechanisms can be made stronger based on state-of-the-art security mechanisms for secure cryptographic protocols. These cybersecurity applications can guarantee high trust in the security and privacy of health data.

Equation 2 : Optimization for Treatment Effectiveness:

 3.4.2. Interpretability and Explainability

In practice, there is a substantial difference between predicting outcomes and recommending treatment decisions (Pandugula et al., 2024). It is critical to understand the reasoning about why a particular outcome is predicted, especially for decision-making in a high-stakes environment like personalized treatment. In healthcare, complicated rules are difficult to communicate to patients and clinicians, increasing the possibility of unintended consequences and medical errors. Interpreting the output of a machine learning algorithm is a challenge for many physicians and is often believed to decrease the risk of clinical adoption. Physicians are less likely to trust and therefore will not adhere if they are unable to understand how care software or a tool has generated a feature analysis. Explainable AI models can provide explicit insights, give predictions and make those predictions simple to comprehend for practitioners, helping them in context to trust the model.

These models can be described based on numerous design choices that go either towards a simple and clear explanation model or a black-box and precise predictive model. In medical cancer treatment, when interpreting an ML model that makes clinical predictions, a simple and understandable explanation model is a trade-off against complex clinical decisions. The need to add simplistic explanations to boost interpretation techniques in cancer treatment models should be approached with caution. Simplifying complex models before all details of scientific studies are understood might simplify clear treatment nuances, change evidence-based judgment logic, and in the absence of full patient follow-up data, will also hinder external validation. Misinterpretation of a category involving ML model-based explanations should be avoided. There is always a balance between reasonable effort expended in model interpretation and treatment complexity. Fig 3 . 4 : Applications of Machine Learning in Personalized Treatment.

3.5. Conclusion

In summary, the development and patient-centered application of machine learning algorithms to make currently known and potentially yet unknown critical treatment decisions tailored to individual patients could rapidly transform healthcare practice globally. Such immense potential makes it urgent to develop the nuts and bolts needed to address real-world complexities. For example, machine learning algorithms need to address privacy concerns, including issues related to consent to analyze personal health data, and also be ethically sound. Since a major criticism of machine learning algorithms is their "black box" nature, the interpretation and communication of machine learning findings would address them. Finally, the translation of the utility of the algorithms not only in terms of improving patient outcomes but also in terms of potentially changing resource utilization and saving costs would make the case for their use easy for providers and healthcare organizations of different capacities and financial resources (Kalisetty et al., 2023).

The potential is also great for research. As we continue to understand the nuances of machine learning, more sophisticated, adaptive, and personalized systems for treatment using these principles could further push the boundaries. Additionally, for broad applicability, ensuring data availability similar to the electronic health record data on which most machine learning algorithms are being tested currently necessitates innovative collaboration with different sectors in the healthcare ecosystem to design the data infrastructure needed to make the best evidence for care available for decision-making. Interdisciplinary collaboration with biostatisticians, data scientists, and specialists involved in understanding the multidisciplinary nature of machine learning and its applications would aid in rapid progress in this area. Given the transformative potential, integrating a patient-centered machine learning approach to treatment strategies could significantly improve long-term outcomes for patients.

3.5.1. Future Directions

Even though machine learning supports personalized treatment planning, numerous open issues are future research directions. Furthermore, it is predicted that the increasing inclusion of real-time analytics and big data in the optimization phase of treatment might enhance the fields of precision medicine and artificial intelligence. Healthcare providers, policymakers, and algorithm designers' collaboration is critical for the appropriate application of personalized medicine planning systems. Healthcare practitioners should be made more conscious of the ability of AI to tailor treatments, rather than having total faith in a machine-based selection method. In exploring the black-box nature of a model or tackling problems of existing bias in the training dataset, future research may include steps such as the use of suitable strategies. Furthermore, for future directions in the usage of AI for personalized treatment planning, one likely area may be the regular upgrading of these tools as new therapies and healthcare information come to light. The widespread use of personalized treatments is expected to be enhanced by merging clinical information and genetics. Patients are becoming more involved in medical discussions and are self-managing their health by drawing on medical knowledge. Machine learning strategies tailored to the desires and characteristics of patients could accelerate this movement. Patients could input information such as their wants and needs, present medical issues, and mental health in the future to access disorders, prognoses, and treatment recommendations based on a synthesized view of existing knowledge. Predictions of treatment effectiveness and prognosis may be uploaded based on physical measurements at home (Sondinti et al., 2023). If patients can access and view this knowledge, they will be able to question their doctors about their care alternatives, issues, and treatment recommendations. Digital and in-person educational classes may help individuals become comfortable employing this information. This is only possible if the ability to train doctors, technology designers, and patients exists.

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