Artificial Neural Networks in Medico-Diagnostics
Bibliographic record
Abstract
Artificial Neural Networks have emerged as the one of the leading algorithmic approaches to solve modern healthcare challenges such as biomarker detection, disease diagnosis and medical imaging. The relatively large success of Artificial Neural Networks within this field can be attributed to the neural networks ability to identify underlying trends and abstract relevant details from a given dataset. This paper presents a comprehensive review of Artificial Neural Networks by identifying the key working components of any Artificial Neuron starting from an external input to its corresponding output received from an activation function, it analyses the different methodologies to network construction such as Supervised Learning, Unsupervised Learning and Reinforcement Learning, while also comparatively reviewing popular network topologies used within neural network based healthcare solutions such as Convolutional Neural Networks and Auto Encoders. Finally, this paper summarizes the current state of Artificial Neural Networks within Medico-Diagnostics, predicts future trends, and identifies upcoming areas of challenges such as overtraining of small datasets, a difficulty in obtaining large medical datasets owing to the amount of time it takes medical professionals to label data and a general lack in the availability of high-quality data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".