Ornell F, Borelli WV, Benzano D, Schuch JB, Moura HF, Sordi AO, et al. The next pandemic: impact of COVID-19 in mental healthcare assistance in a nationwide epidemiological study. Lancet Reg Health Am. 2021;4:100061.
Google Scholar
The Lancet Global Health. Mental health matters. Lancet Glob Health. 2020;8:e1352.
Google Scholar
Abbasi J, Hswen Y. One day, AI could mean better mental health for all. JAMA. 2024;331:1691–4.
Google Scholar
Babic B, Gerke S, Evgeniou T, Cohen IG. Beware explanations from AI in health care. Science. 2021;373:284–6.
Google Scholar
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
Google Scholar
Nature Medicine Editorial Team. How to support the transition to AI-powered healthcare. Nat Med. 2024;30:609–10.
Winter NR, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, et al. A systematic evaluation of machine learning–based biomarkers for major depressive disorder. JAMA Psychiatry. 2024;81:386–95.
Google Scholar
Hauser TU, Skvortsova V, Choudhury MD, Koutsouleris N. The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health. 2022;4:e816–e828.
Google Scholar
Haque A, Milstein A, Fei-Fei L. Illuminating the dark spaces of healthcare with ambient intelligence. Nature. 2020;585:193–202.
Google Scholar
Dhamala E, Yeo BTT, Holmes AJ. One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry. Biol Psychiatry. 2023;93:717–28.
Google Scholar
Montag C, Quintana DS. Digital phenotyping in molecular psychiatry—a missed opportunity? Mol Psychiatry. 2023;28:6–9.
Google Scholar
Topol EJ. Toward the eradication of medical diagnostic errors. Science. 2024;383:eadn9602.
Google Scholar
Mekkes NJ, Groot M, Hoekstra E, de Boer A, Dagkesamanskaia E, Bouwman S, et al. Identification of clinical disease trajectories in neurodegenerative disorders with natural language processing. Nat Med. 2024;30:1143–53.
Google Scholar
Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research. Biol Psychiatry. 2023;93:18–28.
Google Scholar
Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, et al. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res. 2023;327:115265.
Google Scholar
Tokuda T, Yoshimoto J, Shimizu Y, Okada G, Takamura M, Okamoto Y, et al. Identification of depression subtypes and relevant brain regions using a data-driven approach. Sci Rep. 2018;8:14082.
Google Scholar
Voineskos AN, Jacobs GR, Ameis SH. Neuroimaging heterogeneity in psychosis: neurobiological underpinnings and opportunities for prognostic and therapeutic innovation. Biol Psychiatry. 2020;88:95–102.
Google Scholar
Chang M, Womer FY, Gong X, Chen X, Tang L, Feng R, et al. Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning. Mol Psychiatry. 2021;26:2991–3002.
Google Scholar
McCutcheon RA, Harrison PJ, Howes OD, McGuire PK, Taylor DM, Pillinger T. Data-driven taxonomy for antipsychotic medication: a new classification system. Biol Psychiatry. 2023;94:561–8.
Google Scholar
Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, et al. Dimensional neuroimaging endophenotypes: neurobiological representations of disease heterogeneity through machine learning. Biol Psychiatry. 2024;96:564–84.
Google Scholar
Sirkis DW, Bonham LW, Johnson TP, La Joie R, Yokoyama JS. Dissecting the clinical heterogeneity of early-onset Alzheimer’s disease. Mol Psychiatry. 2022;27:2674–88.
Google Scholar
Ashton NJ, Janelidze S, Mattsson-Carlgren N, Binette AP, Strandberg O, Brum WS, et al. Differential roles of Aβ42/40, p-tau231 and p-tau217 for Alzheimer’s trial selection and disease monitoring. Nat Med. 2022;28:2555–62.
Google Scholar
Dwyer DB, Chand GB, Pigoni A, Khuntia A, Wen J, Antoniades M, et al. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Mol Psychiatry. 2023;28:2008–17.
Google Scholar
Chand GB, Singhal P, Dwyer DB, Wen J, Erus G, Doshi J, et al. Schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population. Am J Psychiatry. 2022;179:650–60.
Google Scholar
Habtewold TD, Rodijk LH, Liemburg EJ, Sidorenkov G, Boezen HM, Bruggeman R, et al. A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits. Transl Psychiatry. 2020;10:244.
Google Scholar
Chand GB, Dwyer DB, Erus G, Sotiras A, Varol E, Srinivasan D, et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain. 2020;143:1027–38.
Google Scholar
Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23:28–38.
Google Scholar
Kim Y-K, Park S-C. An alternative approach to future diagnostic standards for major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2021;105:110133.
Google Scholar
Wen J, Fu CHY, Tosun D, Veturi Y, Yang Z, Abdulkadir A, et al. Characterizing heterogeneity in neuroimaging, cognition, clinical symptoms, and genetics among patients with late-life depression. JAMA Psychiatry. 2022;79:464–74.
Google Scholar
Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, et al. Disentangling the individual-shared and individual-specific subspace of altered brain functional connectivity in autism spectrum disorder. Biol Psychiatry. 2024;95:870–80.
Google Scholar
Hwang G, Wen J, Sotardi S, Brodkin ES, Chand GB, Dwyer DB, et al. Assessment of neuroanatomical endophenotypes of autism spectrum disorder and association with characteristics of individuals with schizophrenia and the general population. JAMA Psychiatry. 2023;80:498–507.
Google Scholar
Hwang G. Autism spectrum disorder: time to notice the individuals more than the group. Biol Psychiatry. 2022;92:606–8.
Google Scholar
Aglinskas A, Hartshorne JK, Anzellotti S. Contrastive machine learning reveals the structure of neuroanatomical variation within autism. Science. 2022;376:1070–4.
Google Scholar
Tang S, Sun N, Floris DL, Zhang X, Di Martino A, Yeo BTT. Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics and behavioral study. Biol Psychiatry. 2020;87:1071–82.
Google Scholar
Sandin S, Lichtenstein P, Kuja-Halkola R, Hultman C, Larsson H, Reichenberg A. The heritability of autism spectrum disorder. JAMA. 2017;318:1182–4.
Google Scholar
Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542:348–51.
Google Scholar
Ecker C, Bookheimer SY, Murphy DGM. Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurol. 2015;14:1121–34.
Google Scholar
Barnett EJ, Onete DG, Salekin A, Faraone SV. Genomic machine learning meta-regression: insights on associations of study features with reported model performance. IEEE/ACM Trans Comput Biol Bioinform. 2024;21:169–77.
Google Scholar
Fusar-Poli P, Hijazi Z, Stahl D, Steyerberg EW. The science of prognosis in psychiatry: a review. JAMA Psychiatry. 2018;75:1289.
Google Scholar
Raket LL, Jaskolowski J, Kinon BJ, Brasen JC, Jönsson L, Wehnert A, et al. Dynamic electronic health record detection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study. Lancet Digit Health. 2020;2:e229–e239.
Google Scholar
Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry. 2021;78:195–209.
Google Scholar
Dwyer DB, Buciuman M-O, Ruef A, Kambeitz J, Sen Dong M, Stinson C, et al. Clinical, brain, and multilevel clustering in early psychosis and affective stages. JAMA Psychiatry. 2022;79:677–89.
Google Scholar
Basaraba CN, Scodes JM, Dambreville R, Radigan M, Dachepally P, Gu G, et al. Prediction tool for individual outcome trajectories across the next year in first-episode psychosis in coordinated specialty care. JAMA Psychiatry. 2023;80:49–56.
Google Scholar
Garcia-Argibay M, Zhang-James Y, Cortese S, Lichtenstein P, Larsson H, Faraone SV. Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach. Mol Psychiatry. 2023;28:1232–9.
Google Scholar
Zhu Y, Maikusa N, Radua J, Sämann PG, Fusar-Poli P, Agartz I, et al. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk. Mol Psychiatry. 2024;29:1465–77.
Google Scholar
Trakadis YJ, Sardaar S, Chen A, Fulginiti V, Krishnan A. Machine learning in schizophrenia genomics, a case-control study using 5090 exomes. Am J Med Genet B Neuropsychiatr Genet. 2019;180:103–12.
Google Scholar
Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Mol Psychiatry. 2021;26:70–9.
Google Scholar
Chandrashekar PB, Alatkar S, Wang J, Hoffman GE, He C, Jin T, et al. DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction. Genome Med. 2023;15:88.
Google Scholar
Bienefeld N, Boss JM, Lüthy R, Brodbeck D, Azzati J, Blaser M, et al. Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals. NPJ Digit Med. 2023;6:1–7.
Google Scholar
Imrie F, Davis R, van der Schaar M. Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare. Nat Mach Intell. 2023;5:824–9.
Google Scholar
Kessler RC, Bauer MS, Bishop TM, Bossarte RM, Castro VM, Demler OV, et al. Evaluation of a model to target high-risk psychiatric inpatients for an intensive postdischarge suicide prevention intervention. JAMA Psychiatry. 2023;80:230–40.
Google Scholar
Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn Sci. 2019;23:584–601.
Google Scholar
Ross EL, Bossarte RM, Dobscha SK, Gildea SM, Hwang I, Kennedy CJ, et al. Estimated average treatment effect of psychiatric hospitalization in patients with suicidal behaviors: a precision treatment analysis. JAMA Psychiatry. 2024;81:135–43.
Google Scholar
Dong MS, Rokicki J, Dwyer D, Papiol S, Streit F, Rietschel M, et al. Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis. Transl Psychiatry. 2024;14:1–11.
Google Scholar
Mizrahi L, Choudhary A, Ofer P, Goldberg G, Milanesi E, Kelsoe JR, et al. Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients. Mol Psychiatry. 2023;28:4280–93.
Google Scholar
Poweleit EA, Vaughn SE, Desta Z, Dexheimer JW, Strawn JR, Ramsey LB. Machine learning-based prediction of escitalopram and sertraline side effects with pharmacokinetic data in children and adolescents. Clin Pharmacol Ther. 2024;115:860–70.
Google Scholar
Luo Y, Eran A, Palmer N, Avillach P, Levy-Moonshine A, Szolovits P, et al. A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia. Nat Med. 2020;26:1375–9.
Google Scholar
Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, et al. Illusory generalizability of clinical prediction models. Science. 2024;383:164–7.
Google Scholar
Linardon J, Torous J, Firth J, Cuijpers P, Messer M, Fuller-Tyszkiewicz M. Current evidence on the efficacy of mental health smartphone apps for symptoms of depression and anxiety. A meta-analysis of 176 randomized controlled trials. World Psychiatry. 2024;23:139–49.
Google Scholar
Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. NPJ Digit Med. 2020;3:1–11.
Google Scholar
Heller AS, Shi TC, Ezie CEC, Reneau TR, Baez LM, Gibbons CJ, et al. Association between real-world experiential diversity and positive affect relates to hippocampal–striatal functional connectivity. Nat Neurosci. 2020;23:800–4.
Google Scholar
Kaveh R, Schwendeman C, Pu L, Arias AC, Muller R. Wireless ear EEG to monitor drowsiness. Nat Commun. 2024;15:6520.
Google Scholar
Le-Dong N-N, Martinot J-B, Coumans N, Cuthbert V, Tamisier R, Bailly S, et al. Machine learning–based sleep staging in patients with sleep apnea using a single mandibular movement signal. Am J Respir Crit Care Med. 2021;204:1227–31.
Google Scholar
An P, Zhao J, Du B, Zhao W, Zhang T, Yuan Z. Amplitude-time dual-view fused EEG temporal feature learning for automatic sleep staging. IEEE Trans Neural Netw Learn Syst. 2024;35:6492–506.
Google Scholar
Yang Y, Yuan Y, Zhang G, Wang H, Chen Y-C, Liu Y, et al. Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals. Nat Med. 2022;28:2207–15.
Google Scholar
Watson NF, Fernandez CR. Artificial intelligence and sleep: advancing sleep medicine. Sleep Med Rev. 2021;59:101512.
Google Scholar
Shahin M, Ahmed B, Hamida ST-B, Mulaffer FL, Glos M, Penzel T. Deep learning and insomnia: assisting clinicians with their diagnosis. IEEE J Biomed Health Inform. 2017;21:1546–53.
Google Scholar
Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, et al. Research and application of deep learning-based sleep staging: data, modeling, validation, and clinical practice. Sleep Med Rev. 2024;74:101897.
Google Scholar
Levy J, Álvarez D, Del Campo F, Behar JA. Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry. Nat Commun. 2023;14:4881.
Google Scholar
Balliu B, Douglas C, Seok D, Shenhav L, Wu Y, Chatzopoulou D, et al. Personalized mood prediction from patterns of behavior collected with smartphones. NPJ Digit Med. 2024;7:49.
Google Scholar
Bennett CC, Ross MK, Baek E, Kim D, Leow AD. Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. NPJ Digit Med. 2022;5:181.
Google Scholar
Bringas S, Salomón S, Duque R, Lage C, Montaña JL. Alzheimer’s disease stage identification using deep learning models. J Biomed Inform. 2020;109:103514.
Google Scholar
Mentis A-FA, Lee D, Roussos P. Applications of artificial intelligence−machine learning for detection of stress: a critical overview. Mol Psychiatry. 2024;29:1882–94.
Google Scholar
Gumley AI, Bradstreet S, Ainsworth J, Allan S, Alvarez-Jimenez M, Aucott L, et al. The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia. Lancet Psychiatry. 2022;9:477–86.
Google Scholar
Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. 2023;183:589–96.
Google Scholar
Habicht J, Viswanathan S, Carrington B, Hauser TU, Harper R, Rollwage M. Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nat Med. 2024;30:595–602.
Google Scholar
Sin J. An AI chatbot for talking therapy referrals. Nat Med. 2024;30:350–1.
Google Scholar
Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388:1233–9.
Google Scholar
Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature. 2023;620:172–80.
Google Scholar
OpenAI. ChatGPT (Mar 14 version) [Large language model]. 2023. https://chatgpt.com. Accessed 21 October 2024.
NHS England» Digitally enabled therapies assessment criteria. https://www.england.nhs.uk/mental-health/adults/nhs-talking-therapies/digital/assessment-criteria/. Accessed 6 June 2024.
Torous J, Bucci S, Bell IH, Kessing LV, Faurholt‐Jepsen M, Whelan P, et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021;20:318–35.
Google Scholar
Sun J, Dong Q-X, Wang S-W, Zheng Y-B, Liu X-X, Lu T-S, et al. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr. 2023;87:103705.
Google Scholar
Torous J, Blease C. Generative artificial intelligence in mental health care: potential benefits and current challenges. World Psychiatry. 2024;23:1–2.
Google Scholar
Bone C, Simmonds-Buckley M, Thwaites R, Sandford D, Merzhvynska M, Rubel J, et al. Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data. Lancet Digit Health. 2021;3:e231–e240.
Google Scholar
Koch E, Pardinas AF, O’Connell KS, Selvaggi P, Camacho Collados J, Babic A, et al. How real-world data can facilitate the development of precision medicine treatment in psychiatry. Biol Psychiatry. 2024;96:543–51.
Google Scholar
Desai RJ, Glynn RJ, Solomon SD, Claggett B, Wang SV, Vaduganathan M. Individualized treatment effect prediction with machine learning — salient considerations. NEJM Evid. 2024;3:EVIDoa2300041.
Google Scholar
Subbiah V. The next generation of evidence-based medicine. Nat Med. 2023;29:49–58.
Google Scholar
Yarnell CJ, Fralick M. Heterogeneity of treatment effect — an evolution in subgroup analysis. NEJM Evid. 2024;3:EVIDe2400054.
Google Scholar
Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. Lancet Digit Health. 2022;4:e829–e840.
Google Scholar
Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, et al. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci. 2024;25:111–30.
Google Scholar
Adler-Milstein J, Redelmeier DA, Wachter RM. The limits of clinician vigilance as an AI safety bulwark. JAMA. 2024;331:1173–4.
Google Scholar
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388:1201–8.
Google Scholar
Youssef A, Pencina M, Thakur A, Zhu T, Clifton D, Shah NH. External validation of AI models in health should be replaced with recurring local validation. Nat Med. 2023;29:2686–7.
Google Scholar
Banerji CRS, Chakraborti T, Harbron C, MacArthur BD. Clinical AI tools must convey predictive uncertainty for each individual patient. Nat Med. 2023;29:2996–8.
Google Scholar
Lu T, Liu X, Sun J, Bao Y, Schuller BW, Han Y, et al. Bridging the gap between artificial intelligence and mental health. Sci Bull. 2023;68:1606–10.
Google Scholar
Warraich HJ, Tazbaz T, Califf RM. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA. 2025;333:241–7.
Google Scholar
Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28:1773–84.
Google Scholar
Womersley K, Fulford KBill, Peile E, Koralus P, Handa A. Hearing the patient’s voice in AI-enhanced healthcare. BMJ. 2023;383:p2758.
Google Scholar
Allen NE, Lacey B, Lawlor DA, Pell JP, Gallacher J, Smeeth L, et al. Prospective study design and data analysis in UK biobank. Sci Transl Med. 2024;16:eadf4428.
Google Scholar
Liu X, Gao T, Lu T, Bao Y, Schumann G, Lu L. China brain project: from bench to bedside. Sci Bull. 2023;68:444–7.
Google Scholar
Health TLD. Mental health in the digital age. Lancet Digit Health. 2022;4:e765.
Google Scholar
