# Radiation Oncology/Artificial neural network

Most important types of neural networks that form the basis for most pre-trained models in deep learning:

## Deep Learning

* Jürgen Schmidhuber. Neural Netw. 2015 Jan. Deep learning in neural networks: an overview (PMID: 25462637)

## Machine Learning

* Michael Rowe. Acad Med. 2019 Oct.
An Introduction to Machine Learning for Clinicians (PMID: 31094727)
* Yalin Baştanlar et al. Methods Mol Biol. 2014. Introduction to machine learning (PMID: 24272434)
* Jenni A. M. Sidey-Gibbons and Chris J. Sidey-Gibbons. Machine learning in medicine: a practical introduction (PMID: 30890124)

## Artificial Intelligence

* Rene Y Choi et al. Transl Vis Sci Technol. 2020. Introduction to Machine Learning, Neural Networks, and Deep Learning (PMID: 32704420)

## Computational Intelligence

* Neural Networks
* Fuzzy Systems
* Evolutionary Computation

## Large Language Models
### Examination Performance

* Harvard, 2023 PMID 37356806 -- "Evaluating GPT as an Adjunct for Radiologic Decision Making: GPT-4 Versus GPT-3.5 in a Breast Imaging Pilot" (Rao A, J Am Coll Radiol. 2023 Jun 21;S1546-1440(23)00394-0.)
** ChatGPT3.5 and GPT4 responses for breast cancer screening and breast pain versus ACR Appropriateness Criteria
** Outcome: Open ended score 1.83/2.00 for breast cancer screening. Select-all-that-apply score GPT3.5 89% vs GPT4 98%. For breast pain, 1.12/2.00 and 58% vs 78%
** Conclusion: Feasibility of using LLMs for radiologic decision making
* Brown University; 2023 PMID 37541614 -- "Performance of Three Large Language Models on Dermatology Board Examinations" (Mirza FN, J Invest Dermatol. 2023 Aug 2;S0022-202X(23)02486-7.)
** [No text available via PubMed]
* Rothschild Foundation Hospital, France; 2023 PMID 37537126 -- "Success of ChatGPT, an AI language model, in taking the French language version of the European Board of Ophthalmology examination: A novel approach to medical knowledge assessment" (Panthier C, J Fr Ophtalmol. 2023 Aug 1;S0181-5512(23)00305-4.)
** Performance of ChatGPT on French language version of European Board of Ophthalmology examination
** Outcome: Success rate 91%, across all categories. Rapid answer
** Conclusion: ChatGPT could be a valuable tool in medical education and knowledge assessment
* Mayo Clinic; 2023 PMID 37529688 -- "Evaluating large language models on a highly-specialized topic, radiation oncology physics" (Holmes J, Front Oncol. 2023 Jul 17;13:1219326.)
** Custom exam of 100 radiation oncology physics question. GPT3.5, GPT4, Bard, BLOOMZ vs medical physicists vs non-experts
** Outcome: GPT4 outperformed other LLMs. High level of consistency, whether correct or incorrect
** Conclusion: Potential for LLM to work alongside radiation oncology experts as knowledgeable assistants
* Multi-institutional, 2023 PMID 36929393 Skyler B Johnson et al. "Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information"
** Following expert review, the percentage of overall agreement for accuracy was 100% for NCI answers and 96.9% for ChatGPT outputs for questions 1 through 13 (ĸ = ‒0.03, standard error = 0.08)
** There were few noticeable differences in the number of words or the readability of the answers from NCI or ChatGPT
** Overall, the results suggest that ChatGPT provides accurate information about common cancer myths and misconceptions
* Erlangen, 2023 ArXiv link "Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology" Yixing Huang et al.
** For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 63.65% and 74.57%, respectively