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Advancing Precision Oncology Through Artificial Intelligence

By Waqas Haque, MD, MPH

In this latest edition of The Way Ahead: The Convergence of Technology and Cancer Care, Dr. Waqas Haque shares his perspectives on machine learning models for classifying cancer immunophenotypes in lung cancer; Insilico Medicine's artificial intelligence–driven process behind the development of INS018_055, a promising drug for idiopathic pulmonary fibrosis; the approval of lisocabtagene maraleucel for chronic lymphocytic leukemia/small lymphocytic lymphoma; and more! 

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Expanding Access to Clinical Trials Through Decentralized Methods and Technology With Waqas Haque, MD, MPH, and Shaalan Beg, MD

In this installment of Oncology Data Advisor's Exploring AI in Oncology series, Dr. Waqas Haque speaks with Dr. Shaalan Beg, an Adjunct Associate Professor at the University of Texas (UT) Southwestern, who is spearheading efforts to decentralize clinical trials in oncology and expand access for eligible patients. Dr. Beg describes the tools that are currently in development for decentralizing trials, including the use of artificial intelligence (AI), natural language processing, and telehealth. Additionally, he shares his perspectives on transitioning to a career in industry.  

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New Podcast Series: "Exploring Artificial Intelligence in Oncology"

New podcast series! Oncology Data Advisor is excited to launch our new series, "Exploring Artificial Intelligence in Oncology." Hosted by Waqas Haque, MD, MPH, Member of the Oncology Data Advisor Fellows Forum, this series will delve into the burgeoning roles and uses of AI in cancer care, from pathology to toxicity risk prediction to treatment recommendations and beyond.  Subscribe to Oncology Data Advisor wherever you listen to your podcasts, and stay tuned for more episodes rel...

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Expanding Access to Clinical Trials Through Decentralized Methods and Technology With Waqas Haque, MD, MPH, and Shaalan Beg, MD

In this installment of Oncology Data Advisor's Exploring AI in Oncology series, Dr. Waqas Haque speaks with Dr. Shaalan Beg, an Adjunct Associate Professor at the University of Texas (UT) Southwestern, who is spearheading efforts to decentralize clinical trials in oncology and expand access for eligible patients. Dr. Beg describes the tools that are currently in development for decentralizing trials, including the use of artificial intelligence (AI), natural language processing, and telehealth. Additionally, he shares his perspectives on transitioning to a career in industry.  

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Updates on AI and Deep Learning for Cancer Detection and Outcomes Prediction

By Waqas Haque, MD, MPH

In this latest edition of The Way Ahead: The Convergence of Technology and Cancer Care, Dr. Waqas Haque shares his perspectives on a deep learning model for gallbladder cancer detection, a precision medicine platform for pancreatic ductal adenocarcinoma outcome prediction, and more!  

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Streamlining the Pathology Workflow Through Artificial Intelligence With Waqas Haque, MD, MPH, and Leif Honda of TriMetis

This second episode of Oncology Data Advisor's podcast series, Exploring Artificial Intelligence in Oncology, features Leif Honda, Chief Innovation Officer at TriMetis, a digital pathology company focused on improving patient outcomes by accelerating human tissue-based research, development, and testing. Dr. Waqas Haque, member of the Oncology Data Advisor Fellows Forum, speaks with Mr. Honda about the TriMetis Computer-Assisted Pathology (TCAP) AI platform, including how it was developed and trained, its capabilities for streamlining the pathology workflow, and the ongoing process of preparing it for clinical utility.  

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Streamlining the Pathology Workflow Through Artificial Intelligence With Waqas Haque, MD, MPH, and Leif Honda of TriMetis

This second episode of Oncology Data Advisor's podcast series, Exploring Artificial Intelligence in Oncology, features Leif Honda, Chief Innovation Officer at TriMetis, a digital pathology company focused on improving patient outcomes by accelerating human tissue-based research, development, and testing. Dr. Waqas Haque, member of the Oncology Data Advisor Fellows Forum, speaks with Mr. Honda about the TriMetis Computer-Assisted Pathology (TCAP) AI platform, including how it was developed and trained, its capabilities for streamlining the pathology workflow, and the ongoing process of preparing it for clinical utility.  

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Predicting Immune-Related Adverse Events Through Machine Learning With OncoHost: Dr. Waqas Haque, Dr. Matthew Hadfield, and Dr. Ofer Sharon

In this first episode of Oncology Data Advisor's new podcast series, "Exploring Artificial Intelligence in Cancer Care," Dr. Waqas Haque and Dr. Matthew Hadfield of the OncData Fellows Forum engage in a discussion with Dr. Ofer Sharon, CEO of OncoHost. Dr. Sharon provides an overview of the clinical need for increased knowledge of patients' probabilities of developing immune-related adverse events and explains the PROphet® platform, OncoHost's novel plasma-based, proteomic pattern analysis tool that uses a single blood sample to guide immunotherapy treatment decisions.  

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Predicting Immune-Related Adverse Events Through Machine Learning With OncoHost: Dr. Waqas Haque, Dr. Matthew Hadfield, and Dr. Ofer Sharon

In this first episode of Oncology Data Advisor's new podcast series, "Exploring Artificial Intelligence in Cancer Care," Dr. Waqas Haque and Dr. Matthew Hadfield of the OncData Fellows Forum engage in a discussion with Dr. Ofer Sharon, CEO of OncoHost. Dr. Sharon provides an overview of the clinical need for increased knowledge of patients' probabilities of developing immune-related adverse events and explains the PROphet® platform, OncoHost's novel plasma-based, proteomic pattern analysis tool that uses a single blood sample to guide immunotherapy treatment decisions.  

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Insights from the State of AI Precision Oncology Conference

By Waqas Haque, MD, MPH In a virtual seminar held on December 12 of last year, the State of AI Precision Oncology brought together a group of scientists and oncologists to delve into the transformative impact of artificial intelligence (AI) in oncology. Dr. Doug Flora, the Executive Medical Director of Oncology Services at St. Elizabeth Healthcare and Chief Editor of the new journal, AI in Precision Oncology, curated discussions on early cancer detection, drug discovery, and data management. The...

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Addressing Ethical Concerns Around Artificial Intelligence in Cancer Care With Andrew Hantel, MD

In this interview from the 2023 American Society of Hematology (ASH) Annual Meeting, Dr. Andrew Hantel, Instructor in Medicine at Harvard Medical School, shares more about his presentation on oncologists' perspectives on ethical implications of artificial intelligence (AI) in cancer care, as well as ongoing efforts build a framework for the integration of AI into oncology care delivery.  

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The Way Ahead: The Convergence of Technology and Cancer Care: December 2023 Edition

By Waqas Haque, MD, MPH

In this new post of the Oncology Data Advisor Fellows Forum blog series, "Convergence of Technology and Cancer Care," Waqas Haque, MD, MPH, delves into the latest technological advancements seeking to improve the care and outcomes of patients with cancer. In this edition, Dr. Haque explores the potential of an artificial intelligence–based program for providing treatment recommendations, the FDA's groundbreaking approval of the first cell-based gene therapy for sickle cell disease, and integrating a diagonal approach for improving access to cancer treatments and technologies globally. 

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The Way Ahead: The Convergence of Technology and Cancer Care: November 2023 Edition

By Waqas Haque, MD, MPH This is the first post of our new blog series "The Way Ahead: Convergence of Technology and Cancer Care," in which we will delve into the latest technological advancements seeking to improve the care and outcomes of patients with cancer. In this edition, we explore the performance of a new artificial intelligence (AI) chatbot for providing National Comprehensive Cancer Network (NCCN) treatment recommendations, results of a multi-cancer early detection blood test stud...

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Can Artificial Intelligence Diagnose Skin Lesions? With Philipp Tschandl, MD, PhD

​Philipp Tschandl, MD, PhD, and colleagues found that current artificial intelligence (AI) algorithms that use "deep learning"—a type of machine learning that is based on artificial neural networks—outperform humans, even experts, in the classification of pigmented skin lesions. In this interview with i3 Health, Philipp Tschandl, member of the Vienna Dermatologic Imaging Research (ViDIR) Group of the Medical University of Vienna's Department of Dermatology, discusses the significance of the stud...

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Artificial Intelligence Predicts Symptoms in Cancer Patients

Cancer patients have to deal not only with symptoms of cancer and adverse effects due to treatments, but also with comorbidities such as depression, anxiety, and sleep disturbance. Because these symptoms greatly reduce patients' quality of life, it is imperative to combat them. Scientists have discovered how to better treat these symptoms by utilizing artificial intelligence (AI) in order to predict their presence and severity in cancer patients before they occur, allowing doctors to identify hi...

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AI Beats Human Experts in Classifying Skin Lesions

A new study reports that artificial intelligence (AI) in the form of machine-learning algorithms outperforms human experts in the diagnosis of pigmented skin lesions. This web-based study, which was published in The Lancet Oncology, included 511 human readers from 63 countries. Of these, 55.4% were board-certified dermatologists, 23.1% were dermatology residents, and 16.2% were general practitioners. The human readers were asked to diagnose dermatoscopic images that had been randomly selected in...

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Artificial Intelligence: Useful for Designing Clinical Trials

Due to lackluster recruiting techniques, a suboptimal number of patients are selected to participate in clinical trials. In addition, the researchers often have limited ability to observe and coach patients during clinical trials. These factors contribute to high clinical trial failure rates, which have a negative impact on the drug development cycle, not to mention 10 to 15 years and hundreds of millions of dollars wasted. However, scientists have proposed a potential solution to this problem: ...

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