Artificial Intelligence (AI) in Family Medicine - A Summary

Authors

  • Rohan S Kulkarni

  • Salva G Ahmed

  • Sunil S Kulkarni

  • Virendra K Bhojwani

Keywords:

AI and machine learning in family medicine, AI and machine learning in geriatrics

Abstract

This bibliographic review evaluates Artificial Intelligence (AI) theory's applications in the field of Family Medicine. Globally billions of people suffer from multiple health related issues throughout their lives including diseases of the heart, lungs, kidney, diabetes, and many forms of cancer. Diagnosis, remedy, and prevention of these disorders are multifaceted, and machine/computer based investigative tools for doctors are immediately needed to augment their decision-making. This study includes various applications of AI/machine learning (AI/ML) procedures in family medicine and its various sub-specialties. AI/ML-centered medicine offers better solutions over standard family medicine covering birth through end of life care. These include treatments for adolescents, geriatrics, disorders of pain and sleep, and sports injuries. However, several implementation hurdles for AI in clinical family medicine persist.

Downloads

How to Cite

Artificial Intelligence (AI) in Family Medicine - A Summary. (2023). Global Journal of Medical Research, 23(C3), 13-20. https://doi.org/10.34257/GJMRCVOL23IS3PG13

References

S Kulkarni, R Kulkarni, K Lorenz (2022) Artificial Intelligence (AI) in Psychiatry -A Summary. 22(3), 1-12.

Archana Buch, Rohan Kulkarni (2021) Artificial Intelligence (AI) in Pathology - A Summary and Challenges. (2), 23-34.

Charlotte Blease, Michael Bernstein, Jens Gaab, Ted Kaptchuk, Joe Kossowsky, Kenneth Mandl, Roger Davis, Catherine Desroches (2018) Computerization and the future of primary care: A survey of general practitioners in the UK. 13(12), e0207418.

Nick Summerton, Martin Cansdale (2019) Artificial intelligence and diagnosis in general practice. 69(684), 324-325.

A Baser, S Baktıraltuntaş, G Kolcu (2021) Artificial Intelligence Anxiety of Family Physicians in Turkey. 23(2), 2021275.

Katrina Darcel, Tara Upshaw, Amy Craig-Neil, Jillian Macklin, Carolyn Steele Gray, Timothy Chan, Jennifer Gibson, Andrew Pinto (2023) Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue. 18(2), e0281733.

W Liaw, I Kakadiaris (2020) Artificial Intelligence and Family Medicine: Better Together. 52(1), 8-10.

W Wei, C Tao, G Jiang A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes. 2010, 857-861.

Shankaracharya, Devang Odedra, Subir Samanta, Ambarish Vidyarthi (2012) Computational Intelligence-Based Diagnosis Tool for the Detection of Prediabetes and Type 2 Diabetes in India. 9(1), 55-62.

Dong-Ju Choi, Jin Park, Taqdir Ali, Sungyoung Lee (2020) Artificial intelligence for the diagnosis of heart failure. 3(1), 54.

W Hamilton, T Green, T Martins (2013) Evaluation of risk assessment tools for suspected cancer in general practice: a cohort study. 63(606), 30-36.

Carole Gardener, Gail Ewing, Morag Farquhar (2019) 20 Validation of the support needs approach for patients (SNAP) tool to enable patients with advanced copd to identify and express their support needs to healthcare professionals. 8(3), 367.2-367.

Benji Kurian, Madhukar Trivedi, Bruce Grannemann, Cynthia Claassen, Ella Daly, Prabha Sunderajan (2009) A Computerized Decision Support System for Depression in Primary Care. 11(4), 140-146.

Myriam Tanguay-Sela, David Benrimoh, Christina Popescu, Tamara Perez, Colleen Rollins, Emily Snook, Eryn Lundrigan, Caitrin Armstrong, Kelly Perlman, Robert Fratila, Joseph Mehltretter, Sonia Israel, Monique Champagne, Jérôme Williams, Jade Simard, Sagar Parikh, Jordan Karp, Katherine Heller, Outi Linnaranta, Liliana Cardona, Gustavo Turecki, Howard Margolese (2021) Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for depression treatment using a simulation center. 308, 114336.

N Loskutova, C Lutgen, E Callen (2021) Evaluating a Web-Based Adult ADHD Toolkit for Primary Care Clinicians. 34(4), 741-752.

Matthew Nemesure, Michael Heinz, Raphael Huang, Nicholas Jacobson (2021) Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. 11(1), 1-9.

V Petrauskas, R Jasinevicius, G Damuleviciene (2021) Explainable Artificial Intelligence-Based Decision Support System for Assessing the Nutrition-Related Geriatric Syndromes. 11(24), 11763.

Thavavel Vaiyapuri, E Lydia, Mohamed Sikkandar, Vicente Díaz, Irina Pustokhina, Denis Pustokhin (2021) Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare. 9, 113879-113888.

Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam Shah (2018) Improving palliative care with deep learning. 18(S4), 122.

Liqin Wang, Long Sha, Joshua Lakin, Julie Bynum, David Bates, Pengyu Hong, Li Zhou (2019) Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions. 2(7), e196972.

Lia Rigamonti, Katharina Estel, Tobias Gehlen, Bernd Wolfarth, James Lawrence, David Back (2021) Use of artificial intelligence in sports medicine: a report of 5 fictional cases. 13(1), 13.

H Novatchkov, A Baca (2013) Artificial Intelligence in Sports on the Example of Weight Training. 12, 27-37.

R Bloomfield, H Williams, J Broberg (2019) Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests. 34(10), 2267-2271.

C Goldstein, R Berry, D Kent (2020) Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement. 16(4), 605-607.

Edeh Onyema, Tariq Ahanger, Ghouali Samir, Manish Shrivastava, Manish Maheshwari, Guellil Seghir, Daniel Krah (2022) [Retracted] Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence‐Based Granger Panel Model Approach. 2022(1).

Harry Mcgrath, Colin Flanagan, Liaoyuan Zeng, Yiming Lei (2019) Future of Artificial Intelligence in Anesthetics and Pain Management. 07(11), 111-118.

J Piette, S Newman, S Krein (2022) Artificial Intelligence (AI) to improve chronic pain care: Evidence of AI learning. 6, 100064.

M Salekin, P Mouton, G Zamzmi (2021) Future roles of artificial intelligence in early pain management ofnewborns. 3, 134-145.

Artificial Intelligence (AI) in Family Medicine – A Summary

Published

2023-12-25

How to Cite

Artificial Intelligence (AI) in Family Medicine - A Summary. (2023). Global Journal of Medical Research, 23(C3), 13-20. https://doi.org/10.34257/GJMRCVOL23IS3PG13