Preface

Nanomedicine field is evolving with drug formulation and their multifunctional approaches to act smartly in its therapeutic and diagnostic function . Nanomedicine-based drug therapy, are normally explored at a fixed dose. Which creates insistent challenges at any given point of time as, the drug synergy is time-dependent, dose-dependent and moreover patient-specific. To overcome this challenge application of artificial intelligence (AI) along with nanomedicine can serve as a helping tool for optimizing the drug and dose parameters. Conversion between these two fields enables upgradation of patient data acquisition, improved design of nanomaterials. In cancer the high intratumor and interpatient heterogeneity behaviour is quite difficult to plan for a rational therapeutic design and further to analyse their output is extremely difficult. In this scenario application and integration of AI based approaches such as pattern analysis and algorithms models can bridge the gap, for improved accuracy of diagnostics and therapeutics. With the help of AI algorithms large datasets can be processed, complex patterns can be exploited for improvement of nanotechnology based design for cancer diagnostics and treatments. Application of precision cancer nanomedicine is highly essential as every patient is unique. Patient groups have varied differences, such as age, gender, height, eye color, blood type as well as unique molecular signatures, which leads to different phenotypic changes and wide-ranging of drug responses amongst patients. Further, patients vary substantially with regard to the dosages needed to attain drug synergy, and desirable degree of drug exposure to reach optimal treatment outcomes. Optimization of dosing in oncology highly essential, often dose reductions are implemented to manage treatment-related toxicity and it faces key challenges while translating it to a clinical practice for dosing establishment. This type of challenges can be addresses via recent advances in AI. In this regard, AI plays a critical role in reconciling this space into an actionable treatment response.

In the era of computer aided technology, almost all field are involved with information technology. AI is the amalgamation of computer ethics and bioethics. During application all aspects of research technology pertaining to the their field needs to be ethics free so that they can be freely used for human welfare. These AI enabled novel technologies based therapy needs to be followed at all levels the ethical principles like human privacy, dignity, justice, morality and fair access to the knowledge for possible beneficial of therapy. The book entitled “Artificial Intelligence Based Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics”. by Dr. Fahima Dilnawaz and Dr. Ajit Kumar Behura exemplifies various modes of the application of AI towards cancer nanomedicine and its related aspects of bioethics. This book indeed is a modest effort to the several approaches of cancer nanomedicine having a broad readership that includes researchers, scholars, academicians, clinicians and their allied partners. The authors have made intensive efforts by inviting various reputed contributors to contribute their views.

This book is divided into several chapters: Need of Artificial intelligence in cancer nanomedicine, Application of artificial intelligence for designing cancer nanomedicine, Artificial intelligence and its application in cancer nanomedicine, Artificial intelligent based diagnostic design for precision cancer nanomedicine, Artificial intelligent based nanosensors to compose the patient’s cancer biomarker profile, Artificial Intelligence as a putative tool for newer drug development approach in cancer nanomedicine, Artificial intelligence-enabled model for predicting metastatic potential of cancer cells, Artificial intelligence for cancer nanotheranostics, Ethical dimensions of using artificial intelligence in healthcare. Each chapter provides surfeit of advanced information. Summarily, the book offers collection of qualitative information on various applications of AI based nanomedicine that can be aptly applied for the successful treatment of cancer diseases. The chapters are well articulated with simple language and pictorial demonstrations for better dissemination of knowledge. The authors are hopeful that the collective effort devoted in writing of this book towards dedicated areas of AI based cancer nanomedicine would fascinate great readership.