Personalized Treatment Of Cancer by the Application of Artificial Intelligence (AI)


Abstract

Cancer is a major medical problem worldwide .Due to its high heterogeneity, the use of same drugs or surgical methods in patients with same tumor may have different curative effects leading to need for more accurate treatment methods for tumors & personalized treatment for patients. And here comes the role of Artificial Intelligence(AI). AI has the availability  of high dimensionality datasets coupled with advances in high performance computing, as well as deep learning architecture has lead to an explosion of AI use in various aspects of oncology research. In addition , AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction ,so as to providing the best treatment for individual patients and improving their clinical outcomes.

Introduction

Cancer is a severe threat to human health with a high mortality and a rising incidence rate. the heterogeneity of tumors is high, which can create great challenges in their treatment .Artificial Intelligence is a promising approach that takes individual genetics, environment and lifestyle into account and concentrates on clarifying, diagnosing and treating diseases to create a customized treatment plan for patients .AI has shown extraordinary potential in processing, mining and analysing data and can use the data to develop different models to help achieve PM. After AI is injected into the clinical process, it will improve the detection rate of lesions and make the screening method more effective. Secondly, AI can promote the level of diagnosis by helping doctors distinguish between true and false disease progression . Finally, AI can calculate the advantages and disadvantages of each treatment scheme and provide the best treatment for patients.

                                                MECHANISM






Application of Artificial Intelligence (AI)

  1. Development of Decision Support System(DSS)
  2. Drug Development and validation
  3. Accurate Diagnosis
  4. Customized Treatment
  5. Virtual Assistant
  6. Risk Screening
  7. Remote health monitoring
  8. Prognosis Prediction 

Benefits

  • Personalizing therapies.
  •  Reducing false positives and negatives.
  • Eliminating cancer overtreatment.
  • Identifying tumor types without the need for invasive procedures.

Challenges

  • Biased training data.
  • Difficulties associated with gathering and managing data.
  • Insufficient training data.
  • Ethical concerns and considerations

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